36 of October’s high-impact CVEs were surfaced through the US Cybersecurity and Infrastructure Security Agency (CISA)’s Known Exploited Vulnerabilities (KEV) catalog, 33 were reported in open sources and validated by Insikt Group, and 13 were exclusively surfaced through honeypot data.

The 82 vulnerabilities in this report affected products from 51 vendors, with Citrix and Microsoft together accounting for approximately 15% of the vulnerabilities. The remaining exposure spanned network edge and security management appliances; operating systems and endpoint software; collaboration, content management, and enterprise platforms; and artificial intelligence (AI), developer, and automation tools.

Insikt Group created a Nuclei template to detect the two PHP2Shell vulnerabilities (CVE-2026-109618 and CVE-2026-109619). Insikt Group also published templates for CVE-2026-87902 (WordPress) and CVE-2026-86124 (AutoAgent). These are available to customers in the Recorded Future Intelligence Operations Platform.

Quick reference: October 2026 vulnerability table

All 69 vulnerabilities below were actively exploited or operationally weaponized in October 2026. This table does not include the 13 CVEs that were exclusively surfaced through honeypot data, which are available to Recorded Future Intelligence Platform customers via the CVE Monthly report. The table below also provides examples of public PoCs identified by Insikt Group. These PoCs were not tested for accuracy or efficacy. Vulnerability management teams should exercise caution and verify the validity of PoCs before testing.

Table 1: List of vulnerabilities that were actively exploited in October 2026 based on Recorded Future data (excluding honeypot-sourced CVEs).

Key trends: October 2026

• PHP2Shell reporting covered confirmed honeypot shell execution, a Fragnesia chain yielding root access on 25 servers, and Potassium-associated payload attempts. Other PHP2Shell activity involved suspected preparations for a North Korean attack, which was assessed with moderate confidence as linked to the Lazarus Group. Other exploitation activity this month included Longlegs deploying Warlock ransomware; ransomware groups exploiting PAN-OS; CL-STA-0954 targeting Fortinet products for data theft; PEERSTITCH deployment; Integrity Technology Group-enabled espionage; CyberXero’s AI-assisted exploitation; and ClingSTUN deployment.
• 37 of the 82 vulnerabilities enabled remote code execution (RCE). Collaboration, content-management, and enterprise platforms were the most affected product type, with 12 vulnerabilities. Microsoft had the largest vendor share, with three vulnerabilities.
• Insikt Group identified public proof-of-concept (PoC) exploits or scanners for 37 of the 82 vulnerabilities in this report.
• The most common weakness class was CWE-78 (OS Command Injection), with eight vulnerabilities, followed by CWE-287 (Improper Authentication), with seven. CWE-22 (Path Traversal) and CWE-77 (Command Injection) each affected six vulnerabilities.
• 10 of the 82 vulnerabilities were at least five years old, and the oldest was approximately 16 years old.

Trend analysis

PHP2Shell exploitation enables root access and internal pivoting

In October, two vulnerabilities in PHP, CVE-2026-109618 and CVE-2026-109619 (collectively referred to as “PHP2Shell”), drew attention from threat actors, who also chained them with CVE-2026-46300, a Linux kernel vulnerability known as Fragnesia. Following their disclosure, Insikt Group published multiple notes and detection rules involving PHP2Shell in October. Highlights from our coverage include:

• Root access and internal pivoting: A TTP Instance detailed a campaign chaining CVE-2026-109618 (PHP) and CVE-2026-109619 (PHP) with CVE-2026-46300 (Linux kernel), also known as Fragnesia. Dream Research reported root access on 25 servers, including systems in a US government-affiliated environment. PHP2Shell provided initial access, and Fragnesia enabled privilege escalation. The actor deployed web shells, stole configurations, credentials, and private keys, and maintained reverse Secure Shell (SSH) tunnels to internal systems, enabling service-account password decryption and Active Directory enumeration. Domain-account creation was attempted but not confirmed.
• Technical analysis and exploit development: A separate TTP Instance explained PHP2Shell’s memory-lifecycle failures enabling unauthenticated remote code execution (RCE), highlighting Apache mod_php exposure and unvalidated early PoCs. Separate TTP Instances covering reports from SOCRadar and AkatsukiLegion described actor-staged exploits, prerequisite checks, and a memory-leak-to-command-execution chain.
• Threat actor pre-positioning: Two other TTP Instances covered PHP2Shell tooling on an extortion actor’s staging server and CYFIRMA’s assessment of suspected North Korean preparations using 21 compromised nodes to check 216 hosts. CYFIRMA assessed a Lazarus Group connection with moderate confidence. Neither earlier shared-hosting intrusions nor attack preparations established a successful PHP2Shell compromise.
• Botnet activity: A TTP Instance covering Arctic Wolf and Cisco Talos described probes and payload-download attempts associated with Potassium, a Mirai-derived botnet. In separate TTP Instances covering Linux bots reported by Joe Security and Nextron Research, Insikt Group corroborated remote-shell, evasion, and denial-of-service capabilities in the former, and PHP2Shell testing, scanning, downloads, and persistence in the latter. These findings did not confirm successful malware delivery through PHP2Shell.
• Detection rule coverage: Insikt Group created Sigma rules for Linux and Windows to detect web or PHP workers spawning shells, including PowerShell, and Suricata and Snort rules for multipart session-ID and upload-progress requests. Network detection requires visible HTTP bodies. Correlate these alerts with process creation, worker errors, and outbound activity because individual matches can be benign.
• Separate PHP vulnerability: A TTP Instance reported on CVE-2026-115560 (PHP), a vulnerability distinct from PHP2Shell that could enable unauthenticated RCE in applications switching session serializers while upload progress is enabled. No in-the-wild exploitation was established at the time of publication. PHP recommended disabling session upload progress pending a separate fix; the initial PHP2Shell patches did not address this vulnerability.

Figure 1: Vulnerability Intelligence Card® for CVE-2026-109618 (PHP2Shell) in Recorded Future (Source: Recorded Future)

The Hiring Automation “Doom Loop” Is a Warning

Hiring offers an instructive example of how humans can be sidelined by a process accelerated by unchecked automation. Machine-assisted decision-making has long been used in the hiring process to manage volume and improve fairness and efficiency. However, generative AI has made reading and writing job applications almost completely machine-dominated, leading the CEO of Greenhouse, a human resources platform, to declare that AI had created a "doom loop" in hiring. No one intended to create this doom loop, but the adoption of automation in one area led to accelerated adoption in others, ultimately sidelining humans from hiring decisions.

The hiring doom loop is driven by two mutually reinforcing trends: more applicants are applying for each position, and more companies are relying on automation to sift through the growing volume of applications. Applicants per hire tripled between 2021 and 2024, according to one survey of hiring managers, and Greenhouse reported a 412% increase in applicants per post. Applicants are almost certainly using generative AI to spin up customized applications to hundreds of jobs at once.

Meanwhile, 91% of HR departments report using AI in recruitment, and Greenhouse data shows that the number of human recruiters has dropped by over 50%. The recruiters who remain cannot meaningfully evaluate any single application at this volume, further reducing human decision-making in the hiring process. Some companies now use AI to conduct interviews, meaning applicants may not meet an actual human at the company until very late in the process, if at all.

Despite the intention of using AI to improve efficiency and manage scale, only one-third of recruiters reported improvements in time-to-hire using AI, while 25% reported it got slower. More companies are lengthening probationary periods, a sign that they're increasingly unable to identify unsuitable candidates before hiring them. Many job hunters express frustration with encountering bots instead of real humans at their potential future employer. At the same time, scammers and state-sponsored cybercriminals have taken advantage of this automated landscape to advance candidates whose real objective is to steal secrets or cryptocurrency.

A few factors made hiring one of the first processes to fall victim to the doom loop. Hiring managers were early adopters of automation, using technology like keyword-based filters to parse resumes. At the same time, successful cover letters and resumes tend to follow a formula, which makes them easy to convincingly replicate with generative AI. However, there are two factors that are not unique to hiring that make it susceptible to doom loop dynamics:

• Incentives for speed and scale: as competition increases, candidates are rewarded for applying to more jobs, faster.
• Mutually reinforced automation: as application volume outgrew what individuals could manage, automation spread further into the hiring process. This, in turn, drives candidates to prioritize impressing the bots rather than their future human co-workers.

The hiring doom loop is a valuable case study for managing automation because it forces leaders to account for factors outside of their direct control. Instead of responding to machine volume with more automation, developers must think strategically about the intended outcomes and potential second-order effects to ensure systems remain aligned with human intent.

Figure 1: The anatomy of a doom loop (Source: Recorded Future)

Since the spring of 2026, Insikt Group has observed multiple Russian-language traffer teams that have quickly adapted affiliate-fraud models to capitalize on growing public interest in prediction markets such as PolyMarket and Kalshi. The platforms offered by three such teams — AtlasCore, PolyFucked, and Magnetic Team — provide websites, administrative panels, cryptocurrency payment infrastructure, victim-management capabilities, technical support, and promotional guidance. This model lowers barriers to entry for affiliates and enables campaigns to scale rapidly as new trends attract potential victims. The services combine convincing prediction-market interfaces with established fraud mechanisms designed to manipulate victims and prevent them from recovering deposited funds. These mechanisms include controlled trading outcomes, fabricated market activity, artificial withdrawal restrictions and fees, and the collection of sensitive identity and payment information.

Artificial intelligence (AI) further accelerates this opportunism by reducing the time, cost, and expertise required to adapt campaigns to emerging events. Insikt Group observed threat actors using AI to support multiple stages of their campaigns, including platform development, content creation, social engineering, and victim engagement.

The rapid adoption of prediction markets by multiple traffer teams illustrates a broader threat from mature fraud ecosystems that can pivot toward emerging technologies, financial products, and cultural trends without having to develop entirely new criminal capabilities. Prediction markets are particularly well-suited to this model because legitimate platforms already combine cryptocurrency, speculative financial activity, rapidly changing news events, and social media discussion. Fraud actors can imitate these characteristics to establish credibility and manufacture urgency. As the fraudulent prediction markets gain visibility, additional traffer teams may experiment with the prediction market theme or reuse the underlying tooling for adjacent fraud schemes.

Key Findings

• AtlasCore, PolyFucked, and Magnetic Team supply affiliates with simulated prediction-market websites, administrative panels, payment infrastructure, and promotional support. Advertised compensation includes 70% to 80% of net profits for AtlasCore affiliates, a minimum 60% revenue share for PolyFucked affiliates, and a base 70% share of victim deposits for Magnetic Team affiliates.
• AtlasCore’s Telegram posts recorded approximately $10.97 million in claimed deposits across its scam offerings between May 2 and September 18, 2026, with the deposits predominantly associated with prediction markets. PolyFucked separately advertises approximately $2.4 million in quarterly turnover. Both figures rely on threat actor reporting and remain unverified.
• Magnetic Team advertises more than 3,000 simulated traders across more than 5,000 markets to fabricate convincing trading activity. Its code exposes configurable withdrawal-blocking pretexts, forced-loss controls, and pools of up to 20,000 deposit wallets per blockchain. AtlasCore similarly exposes settings to force wins or losses and impose individualized withdrawal restrictions.
• All three traffer teams advertise or provide AI-assisted victim support. AtlasCore and PolyFucked also supply guidance for AI-generated promotional content, including synthetic influencers and branding, respectively. Magnetic Team’s source code contains indicators of likely AI-assisted development, suggesting AI use may extend to building and maintaining the underlying fraud platforms, although that assessment remains unconfirmed.
• Insikt Group identified over 100 unique domains and subdomains associated with AtlasCore, PolyFucked, and Magnetic Team, including administrative infrastructure and scam websites likely operated by the teams or their affiliates.

Threat Analysis

AtlasCore

In August 2026, Insikt Group identified a post on a low-tier cybercriminal forum seeking affiliates to join the traffer team “AtlasCore”. The thread was first created on May 15, 2026, by the user “AtlasCore”. The affiliate program was also advertised on a top-tier cybercriminal forum by the user “AtlasCorp” on May 28, 2026. The program includes an offering for prediction markets, as well as more typical traffer scams such as cryptocurrency exchanges and casinos.

Figure 1: Advertisement posted by AtlasCore on a cybercrime forum (Source: Recorded Future)

Targeting South Korean Banks

In early October 2026, South Korea’s Financial Services Commission (FSC) disclosed that multiple financial institutions had been hit by cyberattacks. The attacks had compromised different systems and allowed unauthorized access to different data at each victim entity, with one breach exposing the personal information of an outsourced developer and another resulting in the theft of customer data. Industry sources reported that the attacks involved the use of ARTEX, a Chinese-language, open-source AI penetration testing framework. The variety of targets at each financial institution likely demonstrates an advantage of AI over human operators: the ability to opportunistically test for weaknesses at scale until a valuable target is found.

On October 7, 2026, public analysis of the incident identified that the threat actor, suspected to be a Chinese speaker, used several AI tools as part of their campaign, including Claude Code, DeepSeek, Grok, and Zhipu AI. Other reports shared network infrastructure indicators used in the campaign, including a primary server running ARTEX and the use of residential proxies, likely to obfuscate the origin of the activity.

Recorded Future investigated this infrastructure to better understand the actor’s operations and tracked this activity under the designation TAG-217.

Putting Together a More Complete Timeline for TAG-217

Differentiating between agentic and human-operated attacks can be difficult, especially when skilled threat actors use AI to enhance operational effectiveness. Agentic attacks use the same tactics and techniques as human operators, though faster and at scale. In the case of TAG-217, operational security mistakes allowed Insikt Group to piece together how the threat actor used AI tools to amplify their intrusion attempts across multiple environments and to uncover earlier preparations and a broader set of capabilities than public reporting revealed.

TAG-217 routed its traffic through residential internet connections, likely to reduce the chances of detection. Recorded Future analysts used open directories within TAG-217 infrastructure to further understand its operations (Source: Recorded Future)

Initially established in Somerville, MA, north of Boston, the Futurists' headquarters was once a laundromat. Twelve years after we set up camp there, it is time for the next chapter. Our HQ has moved to downtown Boston in Post Office Square — and we’re ready to make new memories there.

Somerville Office laundromat sign | Boston Office (Current) on Franklin Street

Key functions operate out of our Boston-based office, including customer success, security, data engineering, product, and marketing. Nearly 200 Futurists call the Boston office their home base, and many others travel for team meetings and strategic onsites.

Since mid-2025, the Kremlin has intensified restrictions on digital communications and infrastructure, very likely to suppress the organization of domestic political dissent and conduct digital surveillance within Russia. Increasingly strict measures targeting virtual private networks (VPNs), state-managed internet shutdowns, limitations on civilian communications platforms, and monitoring of domestic and foreign entities very likely represent an escalation from the approaches to censorship and surveillance pursued prior to 2024.

However, despite intensifying internet restrictions, technical and political limitations will likely prevent the Kremlin from achieving its long-held objective of a fully “sovereign internet” in the near term. The most likely scenario is that Moscow exerts extensive censorship and monitoring of online activity with increasingly strict –– though not absolute –– controls on circumvention and anonymizing tools.

Insikt Group assesses that the Kremlin’s primary objectives regarding internet restrictions are twofold: facilitating extensive surveillance of online activity to support control over the domestic information space while maintaining connectivity to the global internet, and developing national internet infrastructure under Kremlin control, enabling isolation from the global internet if the Kremlin so chooses. Efforts in support of these objectives have almost certainly provided the Kremlin with enhanced ability to control and monitor domestic online discourse.

In this environment, individuals and organizations operating in Russia are subject to increasing operational risks, including surveillance and exposure of sensitive data, challenges in conducting emergency communications, disruptions to corporate tools and digital applications, and legal risks. Entities with indirect exposure to Russia face an increased likelihood of supply-chain disruptions and sanctions violations and, over the longer term, face challenges in assessing the political and economic situation in Russia, as restrictions limit the availability of high-quality open sources.

Key Findings

• The Kremlin is very likely using managed internet shutdowns across Russia to test and improve its ability to isolate the Russian internet, though technical and political limitations make it unlikely that it will achieve its objective of establishing a fully “sovereign internet” in the near term.
• In the near- to medium-term, a combination of falling approval ratings for Russian President Vladimir Putin, declining public opinion amid economic strain, and ongoing Ukrainian drone strikes very likely incentivize Moscow to impose further internet restrictions, regardless of reported public dissatisfaction with some of these measures.
• The Kremlin will very likely continue to pursue strategies to deter VPN use by forcing platforms to support VPN blocks and making VPN use more expensive and inconvenient, though likely avoiding direct punitive measures against individual users.

Kremlin Systematically Tightening Internet Restrictions

Since July 2025, the Kremlin has systematically tightened access to widely used civilian communications applications and VPNs, caused regular mobile internet outages across Russia, and instituted requirements that place Russian and foreign individuals and firms under increased government surveillance. The Kremlin likely views these measures as necessary to make it more difficult for Russian elites or the public to coordinate political dissent, and for the Ukrainian government to launch drone strikes against Russian critical infrastructure.

Restrictions Represent Marked Escalation in Control Over Russian Internet

Insikt Group assesses that, since 2012, Russians have seen their ability to operate without restriction on the internet decline in several waves, primarily driven by the Kremlin's calculation that Russians were using the internet to organize anti-government political actions.

Table 1: Russian approaches to internet restrictions from 2000–2024 (Source: Recorded Future)

Government Targeting across the Region

TAG-193’s targeting extends across government institutions responsible for diplomacy, economic development, agriculture, and environmental management. These organizations hold information that can inform negotiations, economic planning, and regional security decisions. Across Central Asia, shared river systems and interconnected energy and transport infrastructure give such information particular strategic value.

Figure 1: Timeline of TAG-193 campaigns

The 94 vulnerabilities identified affected products from 58 vendors, with Linux and Microsoft together accounting for approximately 13% of the vulnerabilities. The remaining exposures spanned network edge and security management appliances; operating systems (OS) and mobile devices; collaboration, content management, and enterprise platforms; artificial intelligence (AI), developer, and automation tools; and operational technology and industrial systems.

September reporting also highlighted zero-day exploitation of CVE-2026-88771 and CVE-2026-88772 (NetScaler), as well as renewed exploitation of CVE-2026-35273 (PeopleSoft) against unpatched systems.

Quick reference: September 2026 vulnerability table

All 80 vulnerabilities below were actively exploited or operationally weaponized in September 2026. This table does not include the 14 CVEs that were exclusively surfaced through honeypot data, which are available to Recorded Future Intelligence Platform customers via the CVE Monthly report. The table below also provides examples of public PoCs identified by Insikt Group. These PoCs were not tested for accuracy or efficacy. Vulnerability management teams should exercise caution and verify the validity of PoCs before testing.

Table 1: List of vulnerabilities that were actively exploited in September 2026 based on Recorded Future data (excluding honeypot-sourced CVEs).

Key trends: September 2026

• September reporting linked NightEagle and UNC3569 to GhostContainer and GRAYRABBIT deployments, while Cisco FMC exploitation by three threat clusters led to credential theft, Cyclops Blink deployment, and Qilin ransomware.
• A suspected Chinese-speaking actor used a SecFlow-based environment with Claude-, Qwen-, and DeepSeek-backed workers, and a suspected Russian-speaking actor used hundreds of AI agents to compromise at least 440 PaperCut NG/MF instances associated with 395 organizations.
• 47 of the 94 vulnerabilities enabled remote code execution (RCE). Network-edge and security-management products were the most affected product type. Apache and Google represented the largest share of vendors, with three RCE vulnerabilities each.
• Insikt Group identified public proof-of-concept (PoC) exploits or scanners for 75 of the 94 vulnerabilities identified in September.
• The most common weakness class was CWE-306 (Missing Authentication for Critical Function), with seven vulnerabilities, followed by CWE-22 (Path Traversal), with six. CWE-78 (OS Command Injection), CWE-89 (SQL Injection), and CWE-94 (Code Injection) each affected five vulnerabilities.
• Eight of the 94 vulnerabilities were at least five years old, and the oldest was approximately 12 years old.

Trend analysis

AI-assisted campaigns combine older flaws with post-compromise tooling

Insikt Group detailed a suspected Chinese-speaking actor using a SecFlow-based operations environment with Claude-, Qwen-, and DeepSeek-backed workers to support reconnaissance, exploitation, post-exploitation, and collection across Asian targets. The threat actor tested or staged workflows for CVE-2014-6271, CVE-2020-1938, CVE-2022-22965, CVE-2016-4437, CVE-2021-43798, CVE-2024-4956, CVE-2021-29441, and CVE-2021-44228. The workflow routed candidate findings to vulnerability-specific playbooks, but not every attempt produced confirmed access.

A separate September report described a suspected Russian-speaking threat actor using hundreds of AI agents in a campaign targeting PaperCut NG/MF. The campaign exploited CVE-2026-81578 and CVE-2026-82078, compromising at least 440 instances associated with 395 organizations.

Figure 1: Risk Rules history on the Vulnerability Intelligence Card® for CVE-2022-22965 in Recorded Future (Source: Recorded Future)

This is why malware indicators of compromise work best as evidence within a larger investigation, not as isolated entries on a blocklist. Their value grows when analysts can establish where an indicator came from, how recently it was observed, what threats are connected to it, and whether those connections matter to their organization.

Malware indicators of compromise are technical artifacts or observations that suggest malicious activity. They help security teams detect, investigate, and contain threats, but an IoC is most useful when analysts can assess its source, timing, confidence, and relationship to other threat activity.

Key takeaways

• Malware indicators can include file-based artifacts, network infrastructure, and other technical evidence associated with malicious activity.
• IoCs do not exclusively describe past attacks. They can point to malicious activity that may be underway or has already occurred.
• Indicator types have different defensive lifespans. An IP address may quickly lose relevance, while a file hash remains an exact identifier for the same sample even if attackers create a new version.
• Threat intelligence can add context that helps analysts prioritize IoCs, connect related activity, and move from an isolated match toward a wider investigation.

What are malware indicators of compromise?

An indicator of compromise (IoC) is a technical artifact or observable that suggests malicious activity. The National Institute of Standards and Technology (NIST) defines IoCs broadly enough to include signs that an attack may be imminent, underway, or that a compromise may already have occurred.

For a security operations center (SOC) analyst or incident responder, an IoC can help answer a practical question: Have we seen evidence associated with this threat in our environment?

Analysts can use malware IoCs to validate an alert, search logs and endpoints for related activity, or scope an incident after malicious activity is confirmed. They can also use an indicator as a starting point for investigating connected infrastructure, malware, or attacker behavior.

An IoC does not automatically prove that an organization has been compromised. Context matters. Finding a malware hash executing on an endpoint carries a different meaning from finding the same hash in an intelligence feed.

IoCs and TTPs serve different purposes

Indicators of compromise identify observable evidence associated with malicious activity. Tactics, techniques, and procedures (TTPs) describe how an adversary operates.

A specific command-and-control domain is an IoC. The techniques an attacker repeatedly uses to establish persistence or move through a network are TTPs.

Atomic indicators are often easier to match against security telemetry, but attackers may be able to change them quickly. TTP-based detection can be harder to evade when it focuses on established attacker behavior, but it can also demand richer telemetry and more detection engineering.

Security teams do not need to choose one or the other. An IoC can provide the first concrete lead, while TTPs can help analysts search for related activity after an attacker changes a domain or recompiles malware.

The most common types of malware indicators

Malware indicators generally appear either in files and host activity or in network communications. Each gives analysts a different view of what the malware has done.

File-based IoCs

File hashes such as SHA-256 fingerprints allow security tools to identify an exact known file. They provide a precise match, but attackers can alter a malware sample and generate a new hash.

Other file and host artifacts can reveal how malware operates after reaching a system. Analysts may examine suspicious file names and paths along with changes malware makes to the operating system, such as registry activity or mutex creation.

These artifacts can help investigators determine whether the same malware has appeared elsewhere in the environment.

Network-based IoCs

Network indicators show where malware connects or where malicious content is hosted.

These may include IP addresses associated with command-and-control infrastructure, domains used to distribute malware, or URLs connected to malicious activity. Analysts can also examine characteristics of network traffic when those characteristics are specific enough to the threat.

Network IoCs can help teams find infected systems that contacted known malicious infrastructure. Their usefulness can change over time because infrastructure may be abandoned, reassigned, or shared with legitimate services.

Why raw IoCs have limits

The main problem with raw malware indicators is not that they are useless. It is that the indicator alone often does not tell an analyst enough to make a good decision.

Indicators can lose defensive value quickly

Threat actors can move command-and-control infrastructure, register new domains, or change malware files to avoid known indicators. This makes some IoCs highly time-sensitive.

It is more accurate to think in terms of defensive lifespan than to assume every IoC simply expires. A hash continues to identify the same file, for example, but it stops detecting new versions once an attacker changes the sample.

A malicious label does not provide organizational context

An IP address listed as malicious does not tell a SOC whether its own systems communicated with it, whether the activity is current, or what defensive action is appropriate.

This is where raw feeds can create unnecessary analyst work. Teams must investigate why an indicator was flagged and decide whether that evidence applies to their environment.

Recorded Future recommends combining external indicator context with internal network data and subjecting indicators to organizational vetting before using them for detection in its guidance on indicators of compromise.

More indicators can create more noise

Threat intelligence feeds can contain overlapping, stale, or irrelevant information. Adding feeds without a way to assess their quality can increase alert volume without giving analysts better evidence.

Volume can still matter when a source provides useful threat coverage. It should not serve as the only measure of intelligence quality. Timeliness, confidence, provenance, and relevance also affect whether an indicator deserves attention.

IoCs do not provide complete threat visibility

Threat intelligence can tell analysts a great deal about an indicator that has already been observed. It cannot provide context for infrastructure or malware that its collection sources have never seen.

Better information about known IoCs can improve interpretation, but it does not guarantee complete threat visibility.

How threat intelligence makes malware indicators more useful

Security teams can get more value from IoCs by connecting individual observations to evidence that explains why they matter.

Recorded Future malware intelligence connects malware samples with associated IoCs and threat context. It also supports automated YARA rule generation for malware hunting.

Add context before taking action

Useful context can show when an indicator was observed, what malware is associated with it, and what other infrastructure or activity is connected to it.

Recorded Future uses Risk Scores to help analysts prioritize indicators. These scores can help determine what deserves attention, but they should inform security decisions rather than replace organizational context.

Automate repetitive IoC work

Large indicator volumes make manual lookup difficult. Application programming interfaces (APIs) and security integrations can automate IoC lookup, add threat context, and bring external intelligence into existing security workflows.

Recorded Future Cyber Operations supports these workflows through threat intelligence, APIs, and integrations.

Automation does not need to mean automatic blocking. Teams can automate research and prioritization while applying stronger requirements before an indicator triggers a direct enforcement action.

Turn individual indicators into threat-hunting leads

An IoC often becomes more useful when it leads analysts to related activity.

A malware sample can point investigators toward related variants, behaviors, and infrastructure. Recorded Future malware intelligence includes Auto YARA, which analyzes malware samples for distinctive patterns and generates YARA rules that analysts can use in malware hunting.

This lets teams move beyond searching only for a known hash and look for related files that share useful characteristics. Recorded Future University's guide to malware hunting provides more detail on how threat hunters approach that work.

Best practices for an IoC-driven security program

A useful IoC program should focus on the quality of security decisions rather than the size of the indicator database.

• Check context before enforcement. Establish the source, confidence, and timing of an indicator before deciding whether it should trigger blocking or containment.
• Match automation to the consequence. Automating lookup and prioritization carries less operational risk than automatically blocking infrastructure. Raise the confidence requirement as the action becomes harder to reverse.
• Connect external intelligence with internal evidence. External threat data can show why an indicator deserves attention. Internal telemetry shows whether that threat has reached systems that matter to your organization.
• Review active indicator sets over time. An indicator that no longer belongs in an active detection or blocking rule may still have value for historical investigation. Removing it from active enforcement does not mean deleting the intelligence.

Recorded Future's research into malware and vulnerability trends provides further context on changing malware activity and the threats defenders need to watch.

The future of IoC detection

Attackers can change hashes, IP addresses, and domains to evade known indicators. Security teams, therefore, need ways to connect individual IoCs to related malware activity and attacker behavior.

Automated malware analysis and detection-rule generation can help teams move from a new observation to a broader hunt with less manual work. This does not make atomic indicators obsolete. It gives teams another way to use them alongside behavioral detections and threat context.

Recorded Future combines malware analysis with threat intelligence so security teams can investigate individual IoCs in the context of related malware, infrastructure, and behavior.

Ready to spend less time chasing stale or low-value indicators? Request a demo to see how Recorded Future can help your team add context to malware IoCs and put threat intelligence into existing security workflows.

Artificial intelligence has increasingly become enterprise infrastructure and organizations are now confronting unsanctioned tool use (also known as shadow AI), data exposure, and autonomous behavior that their controls cannot always see.

Recorded Future's AI Infrastructure Indicator Lists focus on closing this gap across risk, IT, and GRC teams whether the goal is policy enforcement, data loss prevention, or detecting shadow AI.

AI is accumulating security debt

The hidden risks of an abundance of AI within an organization are often overshadowed by the daily threats that have to be prioritized by a security team. Last year, we reported on how security debt has been accrued by almost every organization, and the unaddressed risks are piling up faster than teams can service them. As a result, blind spots in AI governance are also increasing.

Take, for example, agentic AI. Its adoption is accelerating rapidly and agents are operating across interconnected systems, giving threat actors more room to exploit vulnerabilities at the organizational level and across supply chains.

Consider a free, viral AI assistant that an employee discovers on social media and installs on their own machine to boost productivity. No procurement process, no security review, no IT visibility.

That's not a hypothetical. OpenClaw, a free open-source AI assistant, saw explosive adoption earlier this year with almost no organizational oversight. Multiply that across a workforce and security teams left trying to govern tools they don't know exist.

By 2027, an estimated 75% of employees are expected to adopt or build technology outside IT governance. As agents become more ubiquitous, defenders need to ensure they can identify which technologies are living on their networks, and begin to track and manage access permissions for autonomous processes in the same way they manage permissions for human users.

Introducing AI Infrastructure Indicator Lists

While many threat feeds give you a wall of indicators and leave you to figure out what they are and whether to trust them, Recorded Future’s AI Infrastructure Indicator Lists do the opposite.

AI Infrastructure Indicator Lists are curated, regularly refreshed datasets that map the AI infrastructure your users, developers, and adversaries are actually reaching for. Indicators include IP addresses, CIDR ranges, and domains, carrying their own Risk Scores and comment fields with service attribution.

Recorded Future provides two distinct AI-related lists: the AI infrastructure list and the Chinese AI infrastructure list. The lists cover a comprehensive range of tools, including general-purpose AI assistants, AI coding assistants, vibe-coding tools, AI search tools, voice and audio AI tools, AI video tools, Chinese-domiciled AI tools, and autonomous AI agents.

Figure 1: List of AI tools monitored in AI Infrastructure Indicator Lists

Japan’s Active Cyber Defense (ACD) framework shifts the country from voluntary information sharing and post-incident investigation to mandatory reporting, preventive communications analysis, and limited disruption of attack infrastructure. Beginning October 1, 2026, designated critical infrastructure operators must notify the government about covered systems and report qualifying incidents; those with existing systems have six months to submit initial notifications. New authorities that allow the government to collect and act on communications information (通信情報) will not take effect until November 23, 2027.

The transition comes amid persistent Chinese strategic collection, North Korean revenue-driven cyber operations, and Russian cyber espionage. In cases directly linked to Japan, indicators related to these threat activities were frequently observed among victims, overseas affiliates, vendors, carriers, hosting providers, and government agencies. We assess with moderate confidence that connecting those indicators quickly enough to identify broader campaigns, warn potentially affected organizations, and coordinate defensive action will be ACD’s principal operational constraint. Its practical reach will extend beyond designated operators: overseas compromises may trigger Japanese reporting duties when they reach covered systems; vendors should expect remediation requests; carriers may be required to assist government measures; and hosting providers may encounter action against malicious systems operating on their infrastructure.

We further assess that improved visibility within covered organizations will likely make foreign subsidiaries, non-designated suppliers, employees, and external platforms comparatively attractive access paths for threat actors without reducing overall attack volume (because initial activity along these routes likely falls outside ACD’s mandatory-reporting perimeter or precedes recognition of a qualifying incident). Companies should therefore treat ACD not as a narrow compliance requirement, but as a change to incident escalation, contracting, evidence preservation, and public-private intelligence sharing across their operations and supply chains.

Key Findings

• Japan’s Active Cyber Defense is a two-statute framework — Acts 42 and 43 of 2025 — organized around four principal pillars: public-private collaboration, government use of communications information, access and neutralization of cyber threats, and institutional restructuring and independent oversight. Act 42 establishes the reporting, information-sharing, communications-analysis, and oversight framework, while Act 43 amends existing laws to authorize neutralization measures and reorganize Japan’s cybersecurity institutions.
• Japan's mandatory cyber incident reporting begins October 1, 2026, but the government's communications-information collection capacity will not enter force until November 23, 2027 — a gap of roughly 13 months between the reporting duty and the detection capability intended to support it.
• Japanese agencies recorded an eleven-fold increase in cyberattack-related packets from 63.2 billion in 2015 to 686.2 billion in 2024, while 99.4% of observed attack packets used overseas source IP addresses. This is equivalent to roughly one attempted attack per IP address every thirteen seconds.
• Chinese, Russian, and North Korean cyber operations targeting Japan reflect distinct primary drivers: China pursues enduring strategic intelligence collection, North Korea prioritizes sanctions-driven revenue generation, and Russia combines standing espionage requirements with disruption that can track Japanese policy decisions.
• Threat actors will likely emphasize five access paths likely to maintain limited visibility under ACD after reporting begins on October 1, 2026: foreign subsidiaries, non-designated suppliers, individual employees, consumer platforms, and short-lived infrastructure distributed across hosting providers. Organizations occupying these positions may consequently face greater targeting despite having no direct ACD reporting obligation.

Background

Japan’s Active Cyber Defense framework (能動的サイバー防御, “ACD”) is a package of legal and institutional reforms intended to prevent serious cyberattacks against government and critical infrastructure. The ACD establishes mechanisms for public-private collaboration (官民連携), government use of specified communications information (通信情報の利用), and narrowly defined authority to access and neutralize systems being used to conduct cyberattacks (アクセス・無害化措置) before they cause significant harm or damage. The framework also restructures the government’s central cybersecurity institutions and establishes independent oversight of the framework’s most sensitive authorities.

The framework is principally established through two statutes passed by the National Diet (国会) on May 16, 2025, and promulgated on May 23, 2025:

• Cyber Response Capability Strengthening Act (サイバー対処能力強化法), Act No. 42 of 2025
  Full statutory title: Act on the Prevention of Damage from Unauthorized Acts against Important Computers (重要電子計算機に対する不正な行為による被害の防止に関する法律).
  This statute establishes the critical-infrastructure reporting, communications information use, public-private information-sharing, and independent oversight framework.
• Cyber Response Capability Strengthening Act Arrangement Act (サイバー対処能力強化法整備法), Act No. 43 of 2025
  Full statutory title: Act on the Arrangement of Related Laws Accompanying Enforcement of the Act on the Prevention of Damage from Unauthorized Acts against Important Computers (重要電子計算機に対する不正な行為による被害の防止に関する法律の施行に伴う関係法律の整備等に関する法律).
  This companion statute amends existing legislation, including the Police Duties Execution Act (警察官職務執行法), Self-Defense Forces Act (自衛隊法), and the 2014 Basic Act on Cybersecurity (サイバーセキュリティ基本法). These amendments authorize access-and-neutralization measures, restructure the government’s cybersecurity institutions, and strengthen interagency coordination.

Implementation is phased; the reorganization of the government’s central cybersecurity institutions took effect on July 1, 2025, and the independent Japan Active Cyber Defense Oversight Commission (サイバー通信情報監理委員会) was established on April 1, 2026. The public-private reporting and access-and-neutralization provisions will take effect on October 1, 2026. Supplementary Article 1(4) of Act No. 42 requires the communications-information use provisions to enter force within two years and six months of promulgation, by November 23, 2027.

The Four Pillars

For explanatory purposes, this overview organizes the ACD framework into four principal pillars:

1. Public-Private Collaboration (官民連携)
2. Government Use of Communications Information (通信情報の利用)
3. Government Access and Neutralization Measures (アクセス・無害化措置)
4. Institutional Restructuring and Independent Oversight (組織・体制整備・独立監督)

Together, the pillars summarize the framework’s principal government authorities, institutional changes, oversight mechanisms, and legal obligations for public- and private-sector organizations. They reflect a fundamental shift in Japan’s cybersecurity posture toward earlier detection, structured public-private cooperation, and preventive disruption, while placing the framework’s most sensitive authorities under specialized independent oversight. They do not encompass every provision within the two statutes.

Pillar 1 — Public-Private Collaboration (官民連携)

Pillar 1 creates mandatory reporting requirements and a consent-based information-sharing framework. For ACD reporting purposes, “designated operators” (特別社会基盤事業者) are critical-infrastructure operators designated under the Economic Security Promotion Act that use covered systems — Specified Important Computers (特定重要電子計算機). These include qualifying operators in sectors such as electricity, telecommunications, transport, and finance. Designated critical-infrastructure operators must notify the responsible government minister when they introduce covered systems — Specified Important Computers — and report qualifying cyber incidents (特定侵害事象等) to that minister and the prime minister. The prime minister will establish a statutory council (協議会)for information sharing and countermeasures to prevent damage to Important Computers (重要電子計算機) — the law’s broader category of protected computer systems, including Specified Important Computers — from specified unauthorized acts. This statutory council will replace and strengthen the existing 2019 Cybersecurity Council (サイバーセキュリティ協議会). Council members may be required to provide relevant materials and must comply with statutory confidentiality requirements.

In practice, two things change under Pillar 1. First, ACD-specific reporting becomes a statutory duty for designated critical-infrastructure operators that use covered systems — including operators in sectors such as electricity, telecommunications, transport, and finance — thereby reducing the government’s dependence on voluntary disclosure once a qualifying incident is detected. Second, government-to-industry sharing enters a protected statutory framework designed to support the distribution of sensitive threat and mitigation information. Pillar 1, therefore, aims to give the government broader visibility into threats while providing participating organizations with actionable warnings. Joining the council is voluntary, but participating members must comply with statutory information-handling and confidentiality obligations and may be required to provide relevant materials.

Pillar 2 — Government Use of Communications Information (通信情報の利用)

Pillar 2 authorizes the government to obtain and analyze specified communications information through two principal pathways:

• Voluntary agreements with participating organizations (当事者協定)
• Defined government measures involving certain foreign-related communications (国外関係通信)

The latter includes foreign-to-foreign communications transiting Japan, as well as limited foreign-to-domestic or domestic-to-foreign communications associated with specified cyber threats. Before a government employee may review acquired data, automated selection (自動選別) must retain only defined mechanical information (機械的情報), such as IP addresses, timestamps, and command information, and delete the remainder. Government measures not based on consent generally require prior approval from the Japan Active Cyber Defense Oversight Commission.

In practice, two things change under Pillar 2. First, the government gains a standing statutory framework for obtaining and analyzing communications information for preventive cyber defense: Japan’s pre-ACD Communications Interception Act authorized interception only for criminal investigations of specified offenses and pursuant to a judge-issued warrant, and Japan lacked a comparable framework for systematic preventive collection and analysis. Second, the limit on what may be retained is enforced by the collection architecture rather than by rules governing analyst conduct. Officials set the selection criteria, but no person may examine the acquired data before the selection is complete, and unselected material must be deleted immediately.

Pillar 3 — Government Access and Neutralization Measures (アクセス・無害化措置)

Pillar 3 establishes access-and-neutralization authority through amendments to the Police Duties Execution Act and Self-Defense Forces Act. Specially designated Cyber Harm Prevention Measure Enforcement Officers (サイバー危害防止措置執行官) may order an administrator to take necessary measures against a computer associated with communications or electronic records identified as being used in malicious cyber activity, or may take those measures directly. Depending on the circumstances, those measures could include:

• Stopping or removing attack software
• Disrupting malicious command-and-control communications
• Changing system settings to prevent an attacker from continuing to control compromised infrastructure

The authority applies when an urgent need exists to prevent serious harm to life, physical safety, or property. Measures generally require prior approval from the Japan Active Cyber Defense Oversight Commission. When circumstances leave no time for advance approval, a measure may proceed, but the Commission must be notified promptly afterward and confirm whether the measure was appropriate.

In practice, Pillar 3 gives designated officers legal authority to intervene against attack infrastructure — including through actions that could otherwise constitute unauthorized access under the 1999 Act on Prohibition of Unauthorized Computer Access — rather than relying on criminal investigation or voluntary remediation. The authority can reach infrastructure outside Japan, but overseas operations are subject to additional restrictions. Police measures against systems not reasonably believed to be in Japan may be conducted only by officers of the National Police Agency (NPA) following consultation with the minister for foreign affairs. In cases involving highly organized and planned threats by a person outside Japan, the prime minister may order the Japan Self-Defense Forces (JSDF) to conduct Communications Protection Measures (通信防護措置) jointly with police, but only when the JSDF’s specialized technology or information is indispensable, and the National Public Safety Commission has requested or consented to JSDF involvement.

Pillar 4 — Institutional Restructuring and Independent Oversight (組織・体制整備・独立監督)

Pillar 4 restructures the government's cybersecurity institutions and creates an independent body to oversee the authorities established in Pillars 2 and 3. The reforms include:

• The National Cybersecurity Office (国家サイバー統括室, NCO) replaces the former National Center of Incident Readiness and Strategy for Cybersecurity (内閣サイバーセキュリティセンター, NISC).
• A new cabinet cyber officer (内閣サイバー官) leads the NCO and coordinates cybersecurity policy across the government.
• The restructured Cybersecurity Strategy Headquarters (サイバーセキュリティ戦略本部) is headed by the prime minister (内閣総理大臣) rather than the chief cabinet secretary, with all other ministers of state as members.
• The Japan Active Cyber Defense Oversight Commission (サイバー通信情報監理委員会) grants prior approvals, conducts ongoing inspections, reviews emergency measures taken without advance approval, and may issue recommendations.

In practice, two things change under Pillar 4. First, central coordination is elevated: the Strategy Headquarters is now chaired by the prime minister rather than the chief cabinet secretary, while the cabinet cyber officer and NCO support government-wide coordination. Second, the sensitive authorities in Pillars 2 and 3 are reviewed by a commission separate from the agencies implementing them. Rather than requiring judicial warrants, the framework uses independent administrative review: the Japan Active Cyber Defense Oversight Commission approves nonconsensual communications acquisition and ordinarily approves neutralization measures, conducts continuing inspections, and receives notification of emergency measures. It may issue recommendations and must report annually to the National Diet through the prime minister and publish a summary. Pillar 4 does not establish a separate offensive cyber authority; cross-border neutralization remains governed by Pillar 3.

Key Limits on ACD Authority

The ACD framework expands Japan’s preventive cybersecurity authorities but does not provide an unrestricted mandate for surveillance or offensive cyber operations. Its principal statutory boundaries include:

• Communications analysis is not general content surveillance. Human review is limited to automatically selected mechanical information, including technical data such as IP addresses, timestamps, and computer commands, meeting statutory criteria. Purely domestic communications are not a category that the government may target without the parties’ consent. If included in data provided under a Party Agreement (当事者協定), they must be excluded through automated selection and deleted without human review. Separate laws continue to govern warrant-based interception for criminal investigations.
• Access and neutralization authority is tied to specific mission objectives outlined in the ACD, rather than blanket provisions for government operators. Measures must address communications or electronic records used or reasonably suspected of being used in malicious activity and must be urgently necessary to prevent serious harm to life, physical safety, or property. Overseas measures carry additional procedural and international-law requirements.
• The JSDF cannot independently initiate Communications Protection Measures. Its involvement is limited to highly organized and planned threats by a person outside Japan that present a high risk of serious, difficult-to-recover disruption, and requires a case-by-case finding that the JSDF’s technology or information is essential in preventing that disruption. Qualifying action requires a prime ministerial order, a request from or the consent of the National Public Safety Commission, and joint implementation with the police.
• Reporting obligations are not universal. They apply to designated critical-infrastructure operators and covered systems, not to every Japanese organization or local government. Local governments instead fall under the separate Local Autonomy Act, which has required them, since April 1, 2026, to establish cybersecurity policies and implement corresponding measures. Qualifying local-government systems may still receive protection under the ACD framework.

Tokyo’s Rationale For the ACD

The ACD framework originates in Japan's 2022 National Security Strategy (NSS), which identified a rapidly deteriorating cyber threat environment and concluded that Japan’s legal authorities and institutional capabilities required expansion. The NSS stated that the “threat of cyberattacks, in which the risk of exposure is relatively low and attackers have an advantage, is growing rapidly.” In essence, cyberattacks were seen as low-risk (for the attackers), potentially high-impact means of disrupting operations, stealing information, or advancing strategic and financial objectives while complicating detection, attribution, and timely government response. The NSS set the objective that Japan’s cybersecurity response capabilities should be “strengthened equal to or surpassing the level of leading Western countries.” Tokyo’s rationale can be organized around three overlapping pressures: a sharp increase in malicious cyber activity observed by Japanese authorities; several high-profile cyberattacks that disrupted Japanese public- and private-sector operations; and gaps in the government’s legal authorities and institutional capabilities for sharing information, analyzing threats, and taking preventive action.

First, Japanese authorities have identified an increase in foreign-origin cyberattacks against Japanese public- and private-sector entities. A September 2025 Japanese NCO briefing document reported, citing the NPA, that 99.4% of observed attack packets in 2024 originated from overseas IP addresses (Figure 1). The same document, citing the National Institute of Information and Communications Technology (NICT), reported an approximately elevenfold increase in cyberattack-related communications between 2015 and 2024, from 63.2 billion observed packets in 2015 to 686.2 billion in 2024. The briefing described this volume as equivalent to approximately one attempted attack per IP address every thirteen seconds, although the underlying measurement represents packets received by sensors rather than confirmed, discrete attacks. Neither dataset identifies actors or intent, and the same briefing explains why the overseas share does not establish that the actors themselves are specifically based overseas: attacks are routed through chains of compromised intermediary machines, so tracing even a domestic-looking source usually leads to an overseas relay. Nevertheless, these figures indicate sustained growth in hostile traffic reaching the Japanese address space.

Figure 1: Reported cyberattack frequency against Japan (machine-translated) (Source: Japanese National Cybersecurity Office)

We know attackers are already using AI to operate at machine speed, running more frequent and more effective attacks. They're automating reconnaissance, standing up infrastructure, and pivoting faster than human analysts can triage an alert. We have seen the cyber kill chain that used to unfold step by step now happens in the blink of an eye, and defenders are struggling to keep up in a fight that no longer gives them time to react.

It's clear that organizations need to reduce risk, defend pre-attack, and act at machine speed. But beyond just moving faster, they may need to rethink defense entirely. Imagine not scrambling to keep pace with the threat landscape, but moving with it: changing as it changes and acting as fast as the attacker acts. That's a world where an autonomous defense system works alongside defenders, applying intelligence at the speed and scale today's threats demand.

Today at Mastercard RiskX in Arizona, we premiered an autonomous defense platform built into Recorded Future. The age of AI action has arrived, and we're excited to show you what's next. Read on to see how this capability came to be, why it's a fundamentally different way to defend, and how you can be a part of it.

From answers to actions

AI for threat intelligence has come a long way. In a short time, analysts and security leaders have gone from searching for answers to finding them fast, thanks to capabilities like AI Insights and Sessions in Recorded Future AI. At the core of it all is the Intelligence Graph®, which turns large sums of data and information into actionable insights, and gives Recorded Future AI the context to deliver the right answer to analysts in seconds.

That success created a new demand: Analysts and business leaders wanted this same intelligence, at the same speed, everywhere they worked — not just inside the Recorded Future Platform. That's why we built Recorded Future Model Context Protocol (MCP). Agents, whether ours or our customers', run across many tools and platforms. This gives them direct, standardized, and cost-efficient access to Recorded Future intelligence.

The Intelligence Graph®, Recorded Future AI's Insights and Sessions, and Recorded Future MCP have laid the foundation for what comes next: defending at the pace adversaries set. To keep up with adversaries, analysts need to act at their speed, confidently taking 10 actions where they once had time for one. They need to spend their time focused on business outcomes, not buried in process. Ultimately, they need a fundamentally different way to use intelligence and act. That's why we're building autonomous defense.

What autonomous defense can do

This is a different way to think about the day-to-day analyst workflow. Analysts can task Recorded Future’s autonomous defense with a goal, not just a question, and it will go from there: drawing on the Intelligence Graph®, deciding what the situation calls for, and delivering outputs directly into your team's existing tools across over 100 integrations. It can act on your behalf, clearing away work so your team can focus on the outcome, not the process.

Autonomous defense runs on the Intelligence Graph® with over 15 years of threat intelligence and analyst research, combined with your own organizational context through your Private Graph, so the system knows what's relevant before anything happens. It's the same intelligence layer customers already use across cyber operations, third-party risk, digital risk protection, and counter-fraud, now can be put to work taking agentic action. When your threat priorities shift or a new vulnerability surfaces, agents already understand why it matters to your environment and start acting in real time.

What this looks like from start to finish: A trigger like a new high-priority threat actor, a malicious website notification, or a critical third-party vulnerability starts the workflow. The Platform reasons through what matters, pulling intelligence and building a hunting package, checking a site's ownership to assess takedown eligibility, or drafting outreach and confirming approvals. Then it can act, including running the hunt and blocking the threat, initiating and monitoring a takedown, or notifying your third party and tracking remediation.

Agents default to fully autonomous execution, because defending at machine speed requires it, but teams that want human review first can dial back to managed autonomy. The result is a system, not just a model, that connects detection, investigation, and response into a single workflow. But a capability like this only matters if you can trust it, which is where Recorded Future’s autonomous defense sets itself apart.

A fundamentally different approach

AI security tools have exploded, but much of the industry feels oversold. Dashboards relabeled. SOAR engines rebranded, chatbots dressed up in analyst clothing. Security teams have grown skeptical of the pattern, and rightly so.

What makes Recorded Future's autonomous defense different?

1. Reasoning models and agent frameworks are becoming commodities. Nearly any vendor can wire up an agent loop. What's scarce is trustworthy, structured intelligence for those agents to reason over. That's what we've spent over 15 years building, and it's the foundation every Recorded Future agent runs on. Frontier models generally lack this training on relevant data.
2. It's grounded in the deepest threat intelligence available. Claims about a threat actor's infrastructure, a vulnerability's exploitation status, or a campaign's targeting come from the Intelligence Graph®: continuous analysis across open, dark, and technical web sources, enriched by Insikt Group's analysts. Not plausible-sounding guesses from general training data. Real intelligence, sourced, traceable, and trustworthy.
3. It's architecturally agentic, not superficially so. Most agents have to poll the world to notice that something has changed. Recorded Future agents subscribe to changes in the Intelligence Graph® and get real-time triggers the moment something relevant moves, so they act on signals instead of burning cycles searching for them. This isn't a chat interface bolted onto a search box. It's purpose-built for how security work actually happens.
4. Every output is auditable. Security teams can't operate on black-box AI. Trust requires transparency. Recorded Future shows its work, so every source, step, and conclusion can be traced. When it flags a threat as high priority, you can see exactly why.
5. Intelligence that ends in a chat window has limited value. Recorded Future delivers outputs into SIEM platforms, ticketing systems, communication tools, and the rest of your security stack where the work actually happens.

This is the beginning

We believe that what we're announcing today is a real leap in what security teams can accomplish: workflows that can eliminate critical threats, hours returned to the analyst, and coverage that wasn't previously achievable. And we're not stopping here.

We're already building expanded multi-agent architectures, where AI agents collaborate on complex, long-running investigations. We're building toward AI that monitors threat groups and updates intelligence continuously over weeks, not sessions. And we're extending the integration layer so Recorded Future AI doesn't just deliver intelligence into your stack, it participates in it.

This is a new era of defense, and we know that many of our customers are as excited about it as we are. If you're a Recorded Future customer and want to be part of what's next, apply for early access here. Autonomous defense is planned for general availability early next year.

That gives defenders an earlier window to act. Ransomware threat intelligence helps security teams identify the actors, infrastructure and access methods connected to ransomware activity before an attack reaches its final stage. Instead of waiting for an endpoint alert or ransom note, teams can look for exposed credentials, malicious infrastructure and known attacker behavior, then act on the threats most relevant to their organization.

The need for that earlier view is growing. Modern ransomware operations may use Ransomware-as-a-Service (RaaS) models and double- or triple-extortion tactics, giving defenders more reason to identify warning signs before encryption.

Key takeaways

• Ransomware threat intelligence can expose signs of an attack before encryption, including compromised access and attacker infrastructure.
• IOCs remain useful, but TTPs provide longer-lasting context because attacker behavior changes less quickly than individual IP addresses or file hashes.
• Early disruption can focus on closing initial access paths or cutting communication between compromised systems and known C2 infrastructure.
• Recorded Future assists in connecting ransomware intelligence with organizational exposure, threat actor context and existing security workflows so teams can better prioritize action.

Why reactive ransomware defense is not enough

Reactive controls remain important, but they often cannot provide the external context security teams need to identify which ransomware threats are most likely to reach their environment.

Endpoint detection and response (EDR), network monitoring, and backups all have a role in ransomware defense. The problem is timing. If a team only acts after malicious behavior appears inside its environment, the attacker may already have gained access or started moving toward systems that matter.

This is where modern ransomware detection benefits from external intelligence. Security teams can compare what they see internally with information about active ransomware groups, infrastructure and exploitation activity outside their network.

IOCs show what happened. TTPs help anticipate what comes next.

Indicators of compromise (IOCs), such as malicious IP addresses, domains, and file hashes, can help security controls identify known threats. They also typically have a short shelf life when attackers rotate infrastructure or alter malware.

Tactics, techniques, and procedures (TTPs) describe how an adversary operates. MITRE ATT&CK organizes those behaviors across stages such as initial access, lateral movement and command and control.

A ransomware actor can quickly replace an IP address. Changing a working attack method takes more effort. Tracking both IOCs and TTPs gives defenders a stronger basis for deciding what to block now and what behavior to watch for next.

The goal is not to replace IOC-based detection. It is to add enough context to understand who may be behind an indicator, how it fits into an attack, and what the adversary is likely to attempt next.

How does Threat Intelligence help prevent ransomware attacks?

The best time to disrupt ransomware is before the attacker reaches the impact stage. Threat intelligence creates opportunities to act during initial access and C2 activity rather than relying on recovery after encryption.

External intelligence can reveal parts of the ransomware operation that are difficult to see from internal telemetry alone. That includes activity in criminal marketplaces as well as infrastructure connected to known threat actors.

Phase 1: Track the adversary outside your network

Initial access is often a business in its own right. Initial access brokers (IABs) obtain access to compromised organizations and advertise it to other criminals. Threat intelligence can surface compromised credentials and exposed Remote Desktop Protocol (RDP) access associated with an organization. It can also monitor IAB listings, actor chatter, and target discussions for warning signs before that access is used.

Monitoring those sources can give security teams a warning that credentials or access associated with their organization are exposed. A credential discovered for sale does not prove that a ransomware attack will follow, but it creates a specific risk that defenders can investigate before someone uses that access.

Threat intelligence can also connect ransomware groups with domains, IP addresses, and other infrastructure associated with their operations. Malware analysis and controlled sandbox testing can add newly observed indicators and behavioral information.

C2 intelligence is especially useful because command-and-control gives attackers a way to communicate with systems they have compromised. If defenders know the infrastructure associated with an active threat actor, they can look for related connections within their own network rather than waiting for the ransomware payload to execute.

Phase 2: Act at the point of disruption

Tracking only matters if the intelligence changes what the security team does.

At initial access, one priority is removing the opportunity before an attacker can use it. If exposed corporate credentials appear in criminal sources, the organization can investigate the affected account, reset credentials where needed, and review authentication activity for signs of misuse.

The same principle applies to vulnerabilities. A large vulnerability backlog does not indicate which flaw an attacker is likely to exploit first. Vulnerability intelligence adds evidence of exploitation activity, enabling teams to respond faster to weaknesses associated with active threats rather than relying on severity scores alone.

C2 creates another intervention point. High-confidence indicators associated with malicious infrastructure can be supplied to security controls so connections are blocked or detected earlier. If an endpoint begins communicating with known C2 infrastructure, the security team can investigate and isolate the affected system before the attacker progresses further.

Disruption does not always require taking criminal infrastructure offline. For an individual organization, breaking the attacker's access to its environment can be enough to stop that attack path.

These controls should sit alongside broader guidance on how to prevent ransomware, including identity protection, patching, and recovery planning. Threat intelligence makes those efforts more targeted by showing teams where an active threat is most likely to intersect with their environment.

Recorded Future turns ransomware intelligence into action

Recorded Future helps security teams connect external ransomware activity with their own exposure so they can spend less time sorting through threat data and more time acting on relevant risks.

Recorded Future’s ransomware mitigation capabilities bring together threat research, dark web intelligence and information about an organization's attack surface. Threat actor, industry and TTP context helps teams narrow their attention to ransomware threats that warrant attention.

Reduce research time with the Intelligence Graph^Ⓡ

A large feed of indicators can contribute to alert fatigue if analysts must investigate each item manually. Recorded Future's Intelligence Graph^Ⓡ connects information about threat actors with associated malware, infrastructure, and vulnerabilities.

That context helps analysts move from an isolated indicator to the relationships around it. Rather than treating every IOC as equally important, teams can assess how an indicator relates to an active threat and whether that threat is relevant to their organization.

Recorded Future uses AI to collect, process, and correlate threat data within the Intelligence Graph. This helps reduce repetitive research and enables teams to prioritize relevant findings.

Focus on the ransomware actors most relevant to your organization

Not every ransomware group has the same targets. Industry, geography and previous victim patterns can help analysts understand where a threat actor tends to focus.

Recorded Future's Ransomware Risk Profile provides a view of an organization's ransomware exposure. Victimology insights help teams understand which ransomware groups and TTPs are most relevant based on targeting patterns such as industry and geography.

This allows SOC and threat intelligence teams to tune detection and hunting around ransomware groups whose past activity makes them more relevant to the business rather than treating the entire ransomware landscape as equally urgent.

Put intelligence into existing security workflows

Threat intelligence loses value when an analyst has to move findings manually from one tool to another.

Recorded Future can deliver intelligence through APIs and integrations with security information and event management (SIEM), security orchestration, automation, and response (SOAR), and endpoint detection and response (EDR) tools. Depending on the configured workflow, intelligence can add context to alerts and support automated blocking of malicious infrastructure.

Build an intelligence-driven ransomware defense

Ransomware threat intelligence changes the point at which defenders can respond. Instead of treating encryption as the first clear sign of an attack, teams can look for the access, infrastructure and behavior that come earlier.

This does not replace EDR, access controls or backups. It gives those defenses external context. A vulnerability becomes more urgent when a ransomware actor is actively exploiting it. An exposed credential becomes an immediate investigation point when criminals are offering access to the organization. A C2 indicator becomes more useful when analysts can connect it to the actor and TTPs behind it.

That earlier context can reduce both the likelihood of a successful attack and the damage an attacker can cause. Given how ransomware affects businesses, moving the disruption point earlier in the attack lifecycle can protect far more than encrypted files.

Ready to stop ransomware before a breach occurs? Explore our product tours to get a feel for how Recorded Future automates the tracking and disruption of the most relevant threat actors targeting your industry.

Ransomware Threat Intelligence FAQs

What is the primary difference between a reactive and a threat intelligence-led disruption strategy for ransomware?

A reactive strategy focuses on detecting and recovering from ransomware after malicious activity reaches the environment. A threat intelligence-led strategy looks for earlier warning signs, such as exposed access, known threat actor behavior and malicious infrastructure, so teams can intervene before encryption occurs.

Is paying a ransom ever considered an effective disruption strategy?

Paying a ransom occurs after an organization has already been compromised, so it is generally not a preemptive disruption strategy. Threat intelligence-led disruption focuses on stopping the attacker earlier in the ransomware lifecycle.

Which stages of the ransomware attack lifecycle offer the best opportunities for external disruption via threat intelligence?

Reconnaissance and command and control (C2) provide valuable opportunities for disruption. Threat intelligence can identify exposed credentials, access being advertised by initial access brokers and infrastructure associated with known ransomware actors before those resources are used against the organization.

How does Recorded Future use AI to accelerate ransomware disruption?

Recorded Future uses AI to collect, process and correlate threat data within the Intelligence Graph. This helps analysts connect indicators with threat actors, infrastructure and TTPs, reducing manual research and helping teams identify ransomware threats that require attention faster.

Can Recorded Future help our organization identify and mitigate our most critical ransomware exposure points?

Recorded Future combines vulnerability and attack surface intelligence with external threat context to help teams identify exposures associated with active ransomware threats. This can help security teams prioritize remediation based on evidence of exploitation and organizational risk rather than severity scores alone.

What is victimology in the context of ransomware intelligence, and how is it used to protect an organization?

Victimology examines the types of organizations ransomware groups have targeted, including patterns related to industry and geography. Security teams can use those patterns alongside threat actor TTPs to identify ransomware groups that may be more relevant to their organization and adjust detection and hunting accordingly.

How is the rise of Ransomware-as-a-Service impacting the way organizations use threat intelligence for tracking?

Ransomware-as-a-Service can separate ransomware operators from the affiliates carrying out attacks. Threat intelligence teams therefore need to track more than a single malware family, including affiliate behavior, initial access activity and criminal-market sources that may reveal how attacks are being prepared.

Recorded Future's Insikt Group^Ⓡ, our team of threat intelligence analysts and security researchers, has been tracking a technique called ClickFix as it works its way into a growing number of brand impersonation campaigns. We recently hosted a webinar digging into that research, and what stood out wasn't just the technique itself. There's no malware automatically installed, no exploit, just a page convincing enough that the victim ends up doing the damage themselves. It is also a strong example of the detection capabilities built into Malicious Site Monitoring, a new use case included in Digital Risk Protection, and a good way to show what those capabilities are actually built to catch.

How ClickFix actually works

ClickFix works by mimicking the visual language people already trust, a CAPTCHA prompt, a familiar logo, a "verify you're human" screen, and using that trust to get someone to run a command on their own machine. There's no code being smuggled past a firewall. The victim is the delivery mechanism.

That's also what makes it hard to catch with traditional tools. A page built to look exactly like a real verification screen doesn't behave like malware, and it doesn't trip the same alarms as a page trying to exploit a browser. It succeeds because the person on the other end believes they're completing something routine.

It's not static, either. The instructions can change depending on the operating system a victim is running, one path for Windows, a different one for macOS, which means the "fix" itself adapts to the target. A single signature or a one-off takedown was never going to keep up with that. Catching this at scale means watching for the pattern, not waiting to recognize a specific file.

Malicious Site Monitoring

This kind of research and the product built to act on it aren't two separate things. Digital Risk Protection's Malicious Site Monitoring is built to catch this exact category of infrastructure, phishing domains, lookalike sites, brand impersonation, fast enough to matter. Disposable infrastructure like this is designed to do its damage and disappear before anyone gets around to reporting it, so speed isn't just a nice-to-have here, it's a necessity.

Underneath that speed is a detection process built in layers. Analyst-built signatures catch known patterns with precision. Content similarity analysis can catch campaigns that move in clusters. Attackers often reuse the same page template across dozens of disposable domains, so even though each domain name looks unrelated, the pages themselves share the same structure underneath. A separate component flags a familiar logo or brand mark through screenshot analysis and Optical Character Recognition (OCR). Machine learning is often able to catch what the other methods might miss, sites that don't resemble any known signature or template, by predicting risk from the page's characteristics rather than requiring a direct match. That layered approach is what makes it possible to evaluate a massive volume of candidate domains and URLs every single day without generating excessive false positives.

From detection to takedown

Finding a threat fast doesn't help much if the next steps are still manual. The real shift in how Digital Risk Protection operates is this: detection, triage, and action now live in the same workflow.

Not every detection needs a human to look at it immediately, and that distinction matters. A multi-stage detection funnel helps filter raw monitoring volume down to those that could need a response, and for malicious sites specifically, an AI Triage Agent reviews flagged detections and delivers a verdict before anything reaches an analyst's queue. Instead of triaging a wall of alerts, teams are looking at a shortlist of things that already have context attached, what was found, why it matters, and what to do about it.

And when something does need action, takedown doesn't require a separate process. Coordination happens directly from the alert, one click, instead of manually chasing down registrars and hosting providers one by one.

What this means going forward

ClickFix is one campaign type. It won't be the last one built this way, and it almost certainly won't be the most sophisticated one we see this year. The interesting story isn't any single technique, it's that the same detection engine tracking it in a research report is the one running in production, watching for the next one before it has a name.

If you want to see that detection-to-action workflow in practice, not just malicious sites, but the full picture across dark web monitoring, code repository exposure, impersonation, and identity monitoring, take a self-guided click-through of Digital Risk Protection or request a demo. If you're already a customer, your account team can walk you through what this looks like against your own brand.

Frequently asked questions

Is ClickFix something Digital Risk Protection can actually detect, or just something Insikt Group writes about?

Both. The detection methodology behind the ClickFix research, layered signature, similarity, and machine learning analysis, is the same engine running inside Malicious Site Monitoring today. The research isn't separate from the product; it's built on it.

Is the AI Triage Agent available for this use case?

Yes. Malicious Site Monitoring is one of two use cases, alongside Dark Web Brand Monitoring, where the AI Triage Agent is live today. It reviews flagged detections and delivers a verdict before anything reaches an analyst's queue.

How do I get access to Digital Risk Protection?

It's available as a standalone solution or as part of Recorded Future's Core, Professional, and Elite platform packages. If you already use Recorded Future for brand monitoring, it's available to activate directly in the platform. If you're new to Recorded Future, request a demo to see it in action.

A great agent gives the decision maker insight into intent, and a reasoning and decision advantage over his or her adversaries. In some circumstances, this advantage shapes destiny. Control over destiny is commonly called sovereignty.

AI has rapidly made reasoning ubiquitous; a commodity accessible to all at ever-decreasing marginal cost. In the age of AI, advantage accrues to those who use reason most effectively, make good decisions most quickly, and take actions most confidently. And who will do the bulk of this reasoning, make these decisions, and take these actions? Agents.

Agent Running in the Age of AI

Historically, agents were sources of intelligence but, in the age of AI, the new consumer of intelligence is an agent. Agents will be dependent on the quality and timeliness of the intelligence they receive to give them context, shape their priorities, drive their behavior, and ensure the quality of their actions. Every agent will need an intelligence layer it can trust. Recorded Future is that layer.

Recruiting and running human agents is hard and expensive, and very few have the resources and the expertise to do it well. One experienced MI6 officer might run a handful of agents at any one time. But the human-to-agent ratio is about to change dramatically as decision-making shifts from carbon to silicon, driving down the costs of agent running, but dramatically increasing our reliance on agent decisions and actions.

The best military and political outcomes often result from the seamless integration of different sources and types of intelligence to produce a true understanding of the threat or the objective to be met, enabling good decision-making and decisive actions. The Cuban Missile Crisis combined geospatial, human, and signals intelligence; the D-Day Landings combined signals, human, and meteorological intelligence. As frontier AI capabilities diffuse, and as agentic systems incorporate these capabilities into multi-agent workflows, the performance of these systems relative to others, and thus their ability to deliver advantage, will be determined by ongoing access to high-fidelity, multi-source intelligence.

This is already the case in the domain of cyber security. Intelligence-enriched systems and workflows learn more quickly, adapt more nimbly, and operate more efficiently and effectively than those they are competing with. The adoption rate of Recorded Future’s AI triage capabilities proves this hands down.

Intelligence platforms like Recorded Future, which shed light on threat actor tactics, techniques, and procedures (TTPs) in real time, and which scale with AI and agent capabilities, are the foundation for effective agentic systems. Intelligence-driven agent running will ensure better agentic decisions and actions, delivering an enduring advantage over adversaries.

As former CIA Director Bill Burns put it, “The foundation for good policy choices is good intelligence wisely used.” In the age of AI, agents will make those choices, and the value of good intelligence will be greater than ever.

We’re incredibly proud of this acknowledgment, just as we are of every recognition we’ve received over the years. Behind every one of these industry evaluations is a lot of work that readers never see — including product demonstrations, customer reference calls, briefing presentations, and formal questionnaire submissions — all of it condensed into a single final report. But the most interesting part of any industry recognition is all the details and information that resides in the demos, decks, and responses, so we wanted to share some additional context on the factors that we believe contributed to our ranking this year.

Use this blog post as a companion guide when reading your complimentary copy of the Forrester Wave™ report, which you’ll find in the link below.

Get the report.

Superior support for Priority Intelligence Requirements

Priority Intelligence Requirements (PIRs) are foundational in helping security teams achieve meaningful outcomes, because they help shape data collection strategy and support the effective decision-making that enables machine-speed defense.

Recorded Future received the highest possible score in the PIR criterion. Forrester’s evaluation describes this score as for vendors that offer “superior support for features such as mechanisms to translate external PIRs meaningfully, advanced querying for building/optimizing PIRs, and structured management of General Intelligence Requirements.”

We’ve made PIRs a particular focus in the Recorded Future Platform, adding prebuilt PIRs and enabling customers to use them or define their own in our Impact and Metrics Dashboard. The dashboard surfaces metrics based on those requirements, so teams can more easily measure and better report on how successfully their programs are answering the key questions business leadership wants answered.

Extensive intelligence collection sources and deep and dark web monitoring

Intelligence is at the core of the modern security stack, and good intelligence is often what separates reactive security teams from proactive ones that can defend themselves pre-attack, at the first sign of threat.

To us, Forrester’s evaluation criteria show how important comprehensive intelligence collection is in providing security teams with full visibility across the threat landscape. Here are the different types of data sources we primarily index and analyze and how these data sources can be used for a multitude of use cases:

1. Technical intelligence — network traffic analysis across billions of daily data points from over 200 points of presence, internet-wide scanning and infrastructure monitoring, malware detonation and behavioral analysis, and vulnerability exploitation tracking
2. Underground intelligence — data gathered from criminal forums, marketplaces, and adversaries that can help identify stolen data and credentials, emerging attack techniques, threat actor intent, and ransomware victimology
3. Community intelligence — aggregated detections across customers that reveal patterns and campaign-level activity no single organization would usually catch on its own
4. Open-source intelligence — broader context from data leakage detection, code repository monitoring, social media monitoring, and web/HTML/DOM analysis to help catch brand abuse, impersonation, and exposed data

Threat hunting, vulnerability intelligence, third-party risk management, and more
We believe that receiving the highest possible scores in the Forrester Wave in criteria around multiple cybersecurity disciplines — including brand protection, third-party and supply chain intelligence, fraud intelligence, and threat hunting and vulnerability intelligence — demonstrate our commitment to providing powerful threat intelligence and defensive capabilities across the entire attack surface.

Figure 1: Unified threat intelligence visibility across the critical attack surfaces

As both human-directed and autonomous AI-powered attacks accelerate, security organizations, policymakers, and industry groups around the world are rethinking traditional approaches to defense.

Recorded Future CISO Jason Steer and Mastercard VP of Government Affairs and Policy Christian Ohanian recently sat down with Recorded Future’s Jon Miller to discuss how to build threat intelligence programs that can defend at machine speed, how emerging policy frameworks can help, and why high-quality intelligence is increasingly essential in this new era.

This blog offers highlights from the discussion. Watch the full event.

Evolving security standards and frameworks
Globally, security frameworks like the National Institute of Standards and Technology (NIST) Cyber AI Profile and Singapore’s cybersecurity guidelines are evolving to help organizations keep pace with new threats. According to Christian Ohanian, the goal is to encourage the adoption of AI to bolster resilience while also providing guidance on the kinds of AI-enabled threats organizations now face.

However, Ohanian pointed out that creating these standards involves debate. While some argue that frameworks should provide a clear, prioritized checklist to help resource-strapped teams improve their security programs, others believe that a "one-size-fits-all" approach fails to account for the unique risk profiles of different industries and organizations.

Jason Steer noted that while standards create a helpful taxonomy, the burden remains on CISOs to translate that language into business-specific risk decisions. “This is why intelligence is important,” he noted. “Every industry, every geography, has its own subtleties of attack, so leaning into ‘What are the real risks to my business?’ becomes the hardest part.”

The role of high-quality, purpose-fit intelligence
Ohanian noted that global security standards increasingly recognize that threat intelligence is a foundational component of modern defense. They’re also beginning to acknowledge that security organizations need to use AI and other autonomous solutions to improve the way they operationalize their intelligence. Framework discussions, he said, are now focused on “the importance of organizations looking really closely at how they can increase the speed of the way they’re using threat intelligence, how they can increase the accuracy of the prioritization of the alerts and warnings they’re getting.”

Ohanian emphasized that organizations must move beyond the simple acquisition of data and evaluate the intelligence sources feeding into their threat intelligence solutions. They need to look at "fit for purpose," ensuring that the intelligence aligns with the organization’s specific risk profile and governance requirements.

Steer agreed. “Coverage and collection at fast speed enable information to be brought together for people to assess the impact to their business,” he said. “But then it's only good if that information gets to the right people in the right tools.”

When it does, he said, high-quality intelligence can better fulfill its ultimate goal of providing “decision advantage”—enabling even less experienced SOC analysts to make quick, effective decisions.

Operationalizing security at machine speed
To begin operationalizing intelligence, organizations need to first know what they’re defending. Do they run software on-premise, in the cloud, or a combination of the two? Once they have a complete picture of the mission-critical systems they need to defend, Steer said, they’ll better understand the threats that might be targeting them.

Then, in order to achieve machine-speed defense against AI-driven attacks, organizations need the right API integrations. That way, they can send enriched, contextual threat data to their vulnerability tools and use AI to identify the clusters of high-risk activity where vulnerabilities intersect with active exploitation.

Steer noted that this shift will eventually allow security leaders to move away from trying to patch every single vulnerability—an impossible task—and toward a strategic, risk-based prioritization model. “Vulnerability prioritization is absolutely primed for AI to streamline and accelerate contextual decision-making at executive levels,” he said.

Driving toward an intelligence-led defense future
The consensus from the panel was clear: The future of cybersecurity lies in the convergence of AI, intelligence-led defense, and robust governance. While the X-factor of new threats—the ones that disrupt even the best-laid plans—will always exist, the goal is to reduce reactive panic and focus on strategic risk reduction.

For security practitioners, the takeaway is two-fold. First, they need to keep a close watch on the evolution of global security frameworks, as these documents will define industry expectations and communication standards, and they’ll give organizations a better understanding of where modern security is going in light of AI.

Second, practitioners need to focus on the quality of intelligence over quantity. By ensuring that their tools are enriched with actionable context, they can empower their teams to move from a state of constant, reactive chaos to one of calculated, proactive defense.

As Jon Miller noted, intelligence-led defense is the best way to ensure that when you move at machine speed, you’re moving in the right direction. “Speed without intelligence just means you're wrong, faster,” he said.

To get a customized walkthrough of Recorded Future’s machine-speed defense in action, take our interactive tour.

This report provides insights and analysis to better understand the role of third-party vendors and guarantee marketplaces from the perspective of Tajin Group, a third-party vendor that advertises and provides services on two Telegram-based Chinese-language guarantee marketplaces, Dabai Guarantee and Xinbi Guarantee. This includes operational challenges, perspectives regarding the competition from other threat groups, and how Tajin Group adapts to changes in its operating environment.

Additionally, we identified that Chinese-language guarantee marketplace users and third-party vendors are increasingly using third-party services to purchase and sell Telegram usernames and anonymous virtual numbers. Through these services, Chinese-speaking criminals can link multiple Telegram usernames and an anonymous virtual number (in lieu of SIM cards) to a single Telegram account. This activity indicates a continued evolution and adaptability among these threat actors, who are strengthening their operational security (OPSEC) measures through tactics such as using anonymous virtual numbers to create Telegram accounts to avoid detection and reach a wider audience.

The phishing, payment card theft, and money laundering activities of Tajin Group, guarantee marketplaces, and their third-party vendors can negatively impact banks, fund transfer services providers, cryptocurrency exchanges, and individuals vulnerable to scam and fraud-related campaigns. As Tajin Group is a single third-party vendor, the potential financial gains in the global payment industry are likely to incentivize other threat groups operating on Chinese-language guarantee marketplaces to conduct campaigns by replicating Tajin Group’s tactics, techniques, and procedures (TTPs) on a global scale.

Key Findings

• Tajin Group is mainly involved in phishing, payment card theft, and money laundering. The group actively targeted mainland Chinese citizens and Chinese banks and demonstrated a nuanced understanding of the prerequisites required to transfer funds overseas or use other payment cards remotely.
• Tajin Group conducts extensive testing involving payment cards belonging to multiple countries on the financial platforms CCAvenue and Geidea. They are well-versed in financial crimes and have listed multiple Bank Identification Numbers (BINs) for payment cards from twelve countries.
• Tajin Group constantly seeks cooperation with other threat groups to use direct payment channels that accept UnionPay, VISA, Mastercard, JCB, and Apple Pay; exploit 2D, 3D, and other payment gateways; and UAE Dirhams and electronic gift cards for their financial theft and money-laundering operations.
• Tajin Group has pivoted from Dabai Guarantee to Xinbi Guarantee, showcasing that third-party vendors do not necessarily stay loyal to a single guarantee marketplace platform. The threat group also detailed their operational challenges, intense competition from competitors, and trust issues with their previous payment card suppliers.
• Operators of Tajin Group have sold and bought at least 100 Telegram usernames and multiple phone numbers from Fragment Market, a platform that facilitates the buying and selling of virtual, anonymous phone numbers and Telegram usernames, further anonymizing their operations.

Background

Guarantee marketplaces have become increasingly popular among Chinese cybercriminals as viable alternatives to Chinese-language dark web marketplaces since Huione Guarantee and its business model gained prominence around 2021. Based on our research and previous reports, we have observed that multiple third-party vendors who are usually involved in advertising the sale of malware, databases, phishing kits, and money laundering services on dark web marketplaces have also begun to use Telegram-based guarantee marketplaces to advertise their services or seek cooperation on these platforms. These marketplaces act as a powerful force multiplier to strengthen cooperation among Chinese-speaking threat groups in recruitment, crowdsourcing information, sharing resources, and deploying personnel to commit crimes that require in-person interactions. For additional insights, see our previous report “Evolution of Chinese-Language Guarantee Telegram Marketplaces”.

We noted that these Chinese-language guarantee marketplaces typically consist of hundreds to thousands of third-party vendors, each operated by different Chinese-speaking threat actors (some syndicates) across the globe. To better understand how third-party vendors operate on these guarantee marketplaces and how they contribute to cybercriminal activities, Insikt Group analyzed the activities of Tajin Group, a threat group that conducts phishing campaigns, payment fraud, money laundering, carding, and exploits 3D and 2D payment gateways, among others. Chinese-language guarantee marketplaces typically follow a strict rule requiring that third-party vendors use only one guarantee marketplace at any given time, and we identified that Tajin Group complied with this rule by ceasing activities on Dabai Guarantee before pivoting to Xinbi Guarantee. Tajin Group observed Chinese etiquette and showed respect for the Chinese-language guarantee marketplace ecosystem, demonstrating that there is likely a common understanding between the operators of these guarantee marketplaces by having a system where no third-party vendors are allowed to make use of multiple platforms to advertise and conduct their campaigns.

Overview of Tajin Group

Tajin Group (踏金集团;踏金 [Tajin] translates to “stepping on gold” in Mandarin) is a Chinese-speaking threat group that focuses on phishing, carding, financial fraud, and money laundering. Insikt Group observed that Tajin Group used to be a third-party vendor operating on Dabai Guarantee from May 2025 to April 2026, but pivoted to Xinbi Guarantee around May 2026. Dabai and Xinbi guarantee marketplaces are known to be populated with third-party vendors that provide various services that facilitate cybercriminal activities, including money-laundering methods and services, the sale of compromised social media and e-commerce accounts, SIM cards, data containing personally identifiable information (PII), malware-as-a-service (MaaS), deepfake technology, know-your-customer (KYC) bypass services, and other illegal activities.

As guarantee marketplaces typically require their vendors to stake cryptocurrency as deposits, Tajin Group claimed to have deposited 208,848 in Tether (USDT) on Xinbi Guarantee. The deposit amount often indicates vendors’ scale of operations, where larger deposit amounts reflect larger-scale operations. By comparison, most vendors on Chinese-language Telegram guarantee marketplaces typically deposit only a few hundred to a thousand USDT. As shown in Figure 1, the deposit amount of 208,848 USDT is significantly larger than that of most other third-party vendors.

Figure 1: Tajin Group’s channel @ttjt claimed that the group is a vendor on Xinbi Guarantee with the corresponding channel @xb8848, and that the group has deposited 208,848 USDT into Xinbi Guarantee Marketplace (Source: Telegram)

Before an intrusion reaches the network, threat actors may research targets, prepare infrastructure, trade stolen credentials, or discuss vulnerabilities they plan to exploit. Security teams that rely solely on internal alerts may miss earlier activity.

Proactive threat intelligence helps security teams identify and assess threats earlier by adding external context about adversaries, infrastructure, vulnerabilities, and emerging activity. That context can help teams decide what deserves attention first and act before a threat develops into a larger incident.

Reactive security still matters: organizations need detection, incident response, and recovery capabilities when attacks occur. But proactive intelligence adds visibility earlier in the process, so security teams are not forced to make every decision after an alert fires.

Key takeaways

• Proactive threat intelligence can reveal adversary activity, infrastructure, and exposure before suspicious behavior appears inside the organization
• Intelligence can show which vulnerabilities, threat actors, and external exposures are most relevant to an organization's environment
• A proactive security mindset informs decisions about patching, threat hunting, security controls, and risk remediation
• Automated collection and analysis can reduce manual intelligence work, so analysts can spend more time investigating relevant threats

How to shift to a proactive security mindset

Reactive security begins when something has already happened. An alert fires, suspicious activity appears, or an incident is confirmed. The security team then investigates what happened and decides how to contain the threat.

Proactive threat intelligence shifts part of that work earlier by helping teams understand which adversaries may target them, which vulnerabilities attackers are exploiting, and what infrastructure or techniques are associated with current campaigns. Instead of waiting for those threats to surface internally, teams can use threat intelligence to prepare and prioritize their response.

The goal is not to predict every attack. It is to reduce uncertainty early enough to make better security decisions.

That distinction matters when teams face more alerts, vulnerabilities, and threat information than they can address at once. Proactive intelligence provides context to determine which risks are most closely connected to the organization's assets, technology, industry, and exposure.

For security leadership, that context can also support risk management. Security leaders can compare threat likelihood, asset importance, and potential business impact rather than treating alert volume as a measure of risk. This helps connect intelligence priorities with CISO-level decisions about people, budget, and remediation.

Steps in a proactive intelligence program

A proactive intelligence program follows four interconnected steps: define intelligence requirements, collect relevant information, analyze it within an organizational context, and turn the findings into security actions.

1. Requirements: Define the security and business questions the intelligence program needs to answer. These may include which adversaries pose the greatest risk, which vulnerabilities need faster action, or where the organization has external exposure.
2. Collection: Gather information that can answer those questions. Internal telemetry remains useful, but proactive intelligence also depends on external visibility. Sources may include open-source intelligence (OSINT), technical forums, dark web sources, and illicit marketplaces where threat activity can appear before an internal alert.
3. Analysis: Connect those signals with organizational context. Analysts assess whether an adversary, vulnerability, or piece of infrastructure is relevant to the organization's assets and current threat profile.
4. Action: Use that context to make a security decision. Teams may change a patching priority, block malicious infrastructure, investigate exposed credentials, or begin a targeted threat hunt.

The cycle continues as threats and business priorities change. For operational cyber threat intelligence to support security operations, the process cannot stop at collection and analysis. Intelligence needs to reach the people and controls that can act on it, then feed new findings back into the next cycle.

Examples of proactive intelligence in action

Proactive intelligence creates value when it changes a security decision. Three use cases show how external context can move action earlier.

Use case 1: Prioritize vulnerabilities using real-world threat activity

Security teams generally cannot treat every published Common Vulnerabilities and Exposures (CVE) record as equally urgent. Severity scores describe the potential technical impact of a vulnerability, but severity alone does not show whether threat actors are exploiting it or how relevant it is to a specific organization.

Proactive threat intelligence adds exploitation context to the decision.

Recorded Future Vulnerability Prioritization provides information about current exploitation and threat activity that can help teams identify which vulnerabilities warrant faster attention. A vulnerability associated with active exploitation may require a different response from another vulnerability with a similar severity score but less evidence of current attacker activity.

This gives teams another factor for deciding what to address first. It also connects threat intelligence and vulnerability management, allowing patching decisions to reflect internal exposure alongside activity in the external threat environment.

Use case 2: Identify external risk before it becomes an internal incident

Not every meaningful security signal appears within systems an organization controls.

Threat actors may register domains that imitate a company, expose employee credentials, or discuss potential targets in criminal communities. Command-and-control infrastructure and other malicious assets can also be prepared outside the organization's environment before they are used in an attack.

Digital risk monitoring gives security teams visibility into that external activity.

Recorded Future Digital Risk Protection monitors external threats including malicious domains, credential exposure, brand impersonation, and dark web activity. Finding those signals earlier gives teams an opportunity to investigate and remediate exposure before it contributes to a larger security problem.

This expands the security team's view from what has already happened internally to what is developing outside the organization.

Use case 3: Give threat hunters a more focused starting point

Threat hunting is proactive, but analysts still need a useful hypothesis about what they are looking for.

Without external context, analysts may spend too much time selecting relevant threat actors, gathering intelligence from multiple sources, validating anomalies, and assembling findings for stakeholders. Proactive intelligence can reduce some of that manual work by giving hunters a clearer starting point.

If intelligence shows that a threat actor is targeting organizations in the same industry, analysts can review that actor's known techniques, malware, and infrastructure and determine which signals are relevant to search for in internal telemetry.

The intelligence does not replace threat hunting. It helps shape the hypothesis and narrow the investigation.

Recorded Future Cyber Operations connects external intelligence with investigation, hunting, detection, and response workflows, helping analysts reach relevant threat context faster. Intelligence can also add context to alerts in SIEM, SOAR, and EDR/XDR tools, helping teams determine whether internal activity relates to known external threats.

How a proactive security mindset reduces business risk

A proactive security mindset can reduce business risk by giving teams more time and context to address relevant threats before they cause greater disruption.

Security teams can direct limited resources toward vulnerabilities being actively exploited, external exposures that require remediation, or adversaries that present a stronger risk to the organization. Earlier context can also improve preparation if a threat later develops into an incident.

For security leaders, this creates a clearer link between intelligence work and business priorities. Instead of measuring value by the number of alerts or reports produced, teams can focus on whether intelligence changed a decision, reduced exposure, or helped security teams act sooner.

Recorded Future: The engine for proactive threat intelligence

External visibility creates its own challenge: scale.

Threat information can appear across public sources, technical data, criminal communities, and internal telemetry. Manually collecting and connecting that information can leave analysts spending more time finding data than deciding what it means.

Recorded Future's Intelligence GraphⓇ addresses that problem by indexing information from more than one million sources and connecting data about threat actors, infrastructure, vulnerabilities, and organizations. The value is not simply in collecting more information, but in connecting signals and adding context so teams can better determine which threats relate to their organization and what response those threats may require. Recorded Future applies that intelligence across security and risk workflows, as the examples below show.

Precision intelligence for different security teams

• Vulnerability Prioritization adds exploitation context to help vulnerability teams prioritize remediation beyond severity scores alone.
• Cyber Operations supports investigation, threat hunting, detection, and response. External intelligence can enrich existing security tools and help teams identify and act on relevant threats with less manual research.
• Digital Risk Protection monitors external exposure such as compromised credentials, malicious domains, and brand impersonation so teams can investigate risks outside infrastructure they directly control.

Recorded Future also extends proactive intelligence into other areas of business risk.

• Third-Party Risk provides external context about vendors and other organizations in the supply chain, helping teams assess changes in third-party exposure.
• Payment Fraud monitors external fraud signals such as compromised payment data and criminal activity so fraud teams can act earlier in the fraud lifecycle.

Together, these capabilities allow teams to apply external intelligence to the decisions they already make rather than treating threat intelligence as a separate research function.

Get ahead of the next threat

Moving beyond a reactive security cycle does not mean trying to predict every attack. It means giving security teams the external visibility and context to identify relevant threats earlier, prioritize them more accurately, and act before the available window closes.

Ready to move beyond the reactive cycle and adopt a proactive security mindset? See the Intelligence Graph in action and learn how proactive threat intelligence can help your team identify and address threats before they develop into major breaches targeting your organization. Schedule a personalized demo today.

Which case does the detective solve faster?

The detective solves the second case much faster than the first, but not because the detective is more intelligent. Instead, the world is more intelligible. The difference is not reasoning ability; it is the organization of knowledge that represents the world and enables action.

Now, imagine two cybersecurity AI agents built on the same frontier language model, with the objective of defending against attacks. Both share a general understanding of vulnerabilities, attackers, and defensive techniques. One, however, operates over fragmented alerts, disconnected data sources, and inconsistent identifiers. The other agent reasons over a structured representation that captures relationships among assets, vulnerabilities, threat actors, detections, and organizational context. Similar to detectives, the difference in performance stems not from greater intelligence but from a more intelligible environment or even a living enterprise model.

This is my primary argument: Agentic intelligence is about constructing a representation of the operational world in which correct decisions become the expected outcome rather than a matter of chance. As agents begin taking actions rather than merely producing answers, the quality of those actions depends mostly on the context the agents inherit and whether it’s structured, current, and backed by trustworthy evidence.

To understand how this works for machines, it helps to examine human learning. Human progress has never depended on information alone. It has depended on organizing that information—deciding which sources to trust, connecting related facts, preserving context, and passing that knowledge forward. We inherit this structure through education, language, institutions, and shared practice. Remove it, and even sophisticated reasoning becomes little more than educated guesswork.

Intelligence doesn’t emerge in isolation. It depends on the quality of the representations we think with. The quality of reasoning depends not only on our ability to draw conclusions, but also on how well the world has been made intelligible before we begin. Trustworthy agents do not emerge from better prompts. They emerge from better operational world models. Humans develop reasoning by building increasingly rich mental models of the world through observation, interaction, and experience. Modern language models follow a different path. They learn statistical representations from vast amounts of data rather than through direct interaction with the environment. As a result, they possess broad knowledge but lack direct access to the current operational state of the world in which they must act.

This distinction is particularly important in cybersecurity. Human analysts continuously integrate observations, institutional knowledge, and information about operational state into a coherent mental model of their environment. AI agents must instead be provided with that operational model. Assets, identities, vulnerabilities, dependencies, relationships, policies, and evolving evidence cannot be inferred reliably from language alone—they must be represented explicitly in the world the agent reasons over. Foundation models are increasingly capable of planning, selecting tools, interpreting observations, and adapting their behavior. Their effectiveness, therefore, depends less on reasoning in isolation than on the quality of the representations they can access and the actions they can take. Those representations encompass not only organizational knowledge, like entities, relationships, provenance, policies, governance, and evolving state, but also the tools available to observe and change that state. Again, general models generate general answers.

During the development of enterprise AI agents at Recorded Future, we encountered many instructive examples. The first lesson came immediately. Early versions of our agents treated open-source information and Recorded Future’s proprietary intelligence with roughly equal weight. The result was generic, internet-scale responses rather than the expert-level analysis we intended. Through repeated experimentation, we changed the architecture so that the agent reasoned primarily over the Recorded Future Intelligence Graph®¹—a structured representation of the cyber threat landscape built from years of analyst research, enriched relationships, and investigative decisions. The agent learned to prioritize high-confidence analytical artifacts, such as Insikt Notes² and curated graph relationships, over weaker signals derived from open sources. The language model itself had not become more intelligent. What changed was the quality of the world it was given to reason about. The result was an agent that produced more authoritative analyses and acted with substantially greater consistency and confidence.

Frontier models are becoming easier to access every month. That means model choice is becoming a weaker source of competitive advantage than many organizations assume. What’s much harder to copy is a trustworthy representation of an organization’s operational knowledge. While production-grade orchestration remains challenging, it is unlikely to provide a durable moat for agentic systems in cybersecurity as frameworks, tooling, and implementation patterns continue to converge. What cannot be commoditized is a trusted operational representation of the organization. Agents struggle wherever knowledge remains implicit—in institutional expertise, undocumented conventions, or relationships no system has captured. Their performance ultimately reflects the quality of the world they can access.

Consider a seemingly simple Priority Intelligence Requirement (PIR): “What is a threat to our organization?” The answer is rarely explicit or universally agreed upon. It emerges from understanding the external threat landscape together with the organization’s own high-value assets, priorities, and risk posture. Constructing that representation is a deliberate analytical exercise, not a by-product of deploying an AI model. Once made explicit, however, it provides the foundation on which agents can reason consistently and act effectively. The differentiating question is therefore not, “Which agent framework do we use?” but, “What operational world do our agents reason over?” Organizations that invest in making their knowledge explicit, structured, and trustworthy will consistently derive more value from AI than those that deploy increasingly sophisticated models against fragmented operational knowledge. In this case, intelligence is truly an architecture rather than computation. To build a structured world for cybersecurity agents to operate in, enterprises should align with the following principles:

Structure before reasoning. An effective AI system does more than retrieve facts. Too many systems repeatedly reconstruct knowledge from unstructured information, wasting computational resources while producing inconsistent results. Instead, intelligent and trusted agents should inherit an organized representation of the world: relationships between entities, events, organizations, and evidence should already exist within a structured living knowledge system. The agent's responsibility is to reason over this established structure, not rediscover it with every interaction. The result can be greater consistency and reliable outcomes, particularly for tasks that depend on relationships across multiple sources. Whether it also lowers total cost depends on how the representation is constructed, maintained, and reused³.

We observed the same pattern when building an autonomous threat hunting capability in our Autonomous Threat Operations (ATO)⁴. The language model already understood cybersecurity concepts—it knew what YARA rules, Sigma rules, MITRE ATT&CK techniques, and behavioral indicators were. The breakthrough did not come from teaching more cybersecurity data. It came from explicitly representing investigations: translating detection rules into behavioral hunts, preserving relationships between evidence, maintaining provenance, and carrying forward prior investigative decisions. An investigation that once required analysts to repeatedly reconstruct context became dramatically faster because the context itself had become part of the system. Before the change, each run reconstructed the relationship between the rule, the relevant behavior, and prior investigative findings. After those relationships became a durable system state, the agent could begin from the current investigative position rather than restarting from the source material. As a result, the agent could act effectively around the clock at greater speed.

Provenance is inseparable from intelligence. Human experts are trained to question sources, recognize outdated information, and detect uncertainty. AI systems cannot make these judgments unless every piece of information carries evidence of its origin, relevance, and reliability. Provenance is what transforms data into trustworthy intelligence⁵. For operational intelligence, provenance should include the source; the precise evidence used and its freshness, confidence, and known contradictions; and the relationship between that evidence and the resulting claim. Without provenance, even sophisticated reasoning is reasoning built on assumptions rather than verified knowledge.

A major pain point for cyber threat intelligence (CTI) teams is that, on any given day, thousands of articles, social media posts, blog entries, and automated alerts may mention an entity of interest to them. Most are operationally irrelevant: news commentary, recycled reporting, speculative claims, or duplicate coverage. The intelligence value lies not in the volume of mentions but in understanding their provenance. Is the information derived from a trusted government advisory, firsthand reporting from an intrusion, or direct observations from a darknet forum? Or is it simply another outlet repeating yesterday’s story? Provenance enables an AI agent to distinguish original evidence from repetition, assess freshness and credibility, and focus attention on developments that materially change the threat landscape rather than amplifying noise.

Verification must be easier than investigation. Trust matters more than raw capability. An AI system that produces impressive answers but cannot explain how it reached them creates uncertainty rather than confidence. Every recommendation should be accompanied by sufficient evidence to make verification substantially easier than reproducing the investigation itself. This includes the observations used, material assumptions, uncertainty, contradictory evidence considered, and the conditions under which the recommendation should not be followed. If it doesn't, the technology has merely shifted labor instead of reducing it.

Transparency is the foundation on which trust is built. This principle increasingly aligns with emerging thinking in frontier AI governance, where trust depends not only on model capability but on rigorous verification of the evidence, processes, and claims underlying AI systems⁶.

A CISO, good friend, operating under significant resource constraints, once asked whether an AI agent could reliably recommend isolating compromised production servers. My answer was yes—but only under one condition. A threat analyst should never have to reconstruct the entire investigation just to decide whether to trust a recommendation coming from an AI agent (it is also very costly). Instead, the AI agent should present the evidence for the underlying conclusion, like the suspicious process it observed, the connection to known malicious infrastructure, the behavioral detections that fired, the supporting threat intelligence, the confidence of each finding, and any contradictory evidence it considered, but also the reasoning that connects those observations. Evidence alone is insufficient; the same facts can often support multiple competing hypotheses.

The goal is not for analysts to reconstruct every investigation from first principles, but to quickly assess whether the agent’s chain of reasoning is sound and whether the recommended action is proportionate to the available evidence. This becomes even more important as agents transition from advisory to autonomous roles. Defensive agents offered by the industry will increasingly be expected to isolate hosts, revoke credentials, or block communications before a human can intervene. In those settings, trust cannot depend on manual verification of every decision. It must be earned through consistently reliable reasoning, calibrated confidence, transparent evidence, and well-defined operational guardrails. Verifying the recommendation should take minutes; reconstructing it from raw telemetry could take hours. Otherwise, the AI has not reduced the workload—it has merely shifted it from investigation to verification.

Efficiency is an intellectual discipline. Many assume that more reasoning necessarily produces better intelligence, yet excessive computation often reflects poor system design rather than greater capability. Efficient intelligence is not about thinking longer; it is about thinking more precisely. A well-designed agent retrieves only what is relevant, avoids unnecessary repetition, and allocates computational effort where it genuinely adds value. Efficiency is not merely an economic concern—it is a measure of intellectual discipline⁷.

In my ongoing conversations with security leaders, one concern has surfaced repeatedly: Organizations cannot afford to reproduce the inefficiencies of alert fatigue with AI agents. Replacing thousands of alerts with thousands of autonomous actions would simply automate the noise. Agents are only as effective as the intelligence, policies, and evidence they operate upon. If the underlying representations contain duplicates, false positives, stale context, or conflicting evidence, autonomous systems will amplify those weaknesses at machine speed. The goal is therefore not to maximize agent activity but to maximize decision quality. The most valuable agent consistently performs the right actions while knowing when not to act.

Reasoning must be preserved, not just data. As organizations deploy multiple intelligent agents, preserving reasoning becomes as important as preserving data. What must persist is not an unrestricted transcript of prior reasoning, but a governed record of evidence examined, hypotheses considered, assumptions made, decisions taken, unresolved questions, confidence levels, and conclusions that have subsequently become stale or invalid. Intelligence is cumulative. Human collaboration succeeds because people exchange not only conclusions but also assumptions, uncertainties, confidence levels, and unresolved questions. Intelligent agents should communicate in the same manner: Each agent should inherit the complete investigative state rather than merely a final answer. This enables cumulative reasoning—each participant building on prior understanding rather than restarting the thinking process—and creates consistency, transparency, and trust across increasingly complex systems.

One of the most common cyber cases I encounter is a ransomware investigation involving multiple analysts, shifts, and, increasingly, specialized AI agents. Whether the next participant is human or machine, they should not begin with only the latest conclusion. They need to inherit the investigative state: the evidence already examined, assumptions made, confidence assigned, competing hypotheses, unresolved questions, and conclusions that have since been revised as new evidence emerged. Without that shared state, every handoff requires reconstructing why earlier decisions were made and which lines of inquiry were already explored. With it, the investigation progresses cumulatively rather than repeatedly restarting, reducing duplication while improving consistency, auditability, and decision quality.

Intelligence must outlive the interface. Technology platforms, user interfaces, and AI models will inevitably evolve, but high-quality intelligence should remain portable, structured, and universally accessible. Organizations should not become dependent on a single application or agent. Intelligence should function as a foundational layer that supports any workflow, regardless of where or how it is consumed.

Threat intelligence organizations have spent years enriching adversary profiles, linking infrastructure, documenting investigative decisions, and building behavioral knowledge—not to mention identifying all the IT debt organizations have. If that intelligence exists only inside one vendor’s application or is encoded in prompts for a single AI assistant, every platform migration forces the organization to rebuild years of accumulated knowledge. By contrast, when intelligence is represented independently (as structured entities, relationships, evidence, and provenance), it can support any analyst, detection engine, hunting agent, or future AI model without having to be recreated. Customer relationships, business policies, operational knowledge, and historical decisions should not be lost when an organization replaces its CRM, SIEM, or AI provider. The applications may change; the intelligence they rely upon should not. Durable intelligence becomes a shared asset that every future system can inherit. Every major platform migration exposes the same problem: Organizations have preserved their applications better than their intelligence.

None of this diminishes the human role; in fact, it centers it. Rather than forcing analysts to build every investigation from scratch, intelligent systems should prepare a structured foundation upon which human expertise can operate. AI becomes a collaborator rather than a substitute. The highest value of an intelligent agent is not that it thinks independently, but that it enables humans to think more effectively.

For decades, we have treated intelligence as something people consume: reports, recommendations, indicators, documents. Agentic systems are forcing us to take a different view. Intelligence has become part of the environment in which reasoning occurs. The competitive advantage will not belong to organizations possessing the largest models. It will belong to those who build trustworthy systems where knowledge is structured, evidence is transparent, reasoning is efficient, and human judgment remains central.

Models will change. Interfaces will disappear. Workflows will evolve. What remains is the architecture of enterprise intelligence itself.

The 73 vulnerabilities in this blog affected products from 45 vendors, with Microsoft accounting for approximately 11% of the vulnerabilities. The remaining exposure spanned remote monitoring and management, virtualization, application delivery, collaboration, artificial intelligence, developer, analytics, identity, operational technology, content management, network edge, video surveillance, and endpoint technologies.

In August, Insikt Group created Nuclei templates to detect CVE-2025-62593 (Ray), CVE-2026-72898 (Metabase), and CVE-2026-9198 (IBM Langflow). Each of these vulnerabilities is featured in this blog. Additionally, Insikt Group had previously created templates to detect CVE-2026-3395 (MaxSite CMS) and CVE-2026-59800 (decolua 9Router), but their exploitation was reported in July, so they are not listed in the August 2026 Vulnerability Table. Additionally, Insikt Group created a Nuclei template to detect GitHub Issue #4255 affecting Apache Log4j, a deserialization allowlist bypass that Apache classified as a hardening gap rather than a Log4j vulnerability; as such, it was not assigned a CVE. These Nuclei templates are available to customers via the Recorded Future Intelligence Platform.

Quick reference: August 2026 vulnerability table

All 70 vulnerabilities below were actively exploited or operationally weaponized in August 2026. This table does not include the three CVEs that were primarily surfaced through honeypot data, which are available to Recorded Future Intelligence Platform customers via the CVE Monthly report. The table below also provides examples of public PoCs identified by Insikt Group. These PoCs were not tested for accuracy or efficacy. Vulnerability management teams should exercise caution and verify the validity of PoCs before testing.

Table 1: List of vulnerabilities that were actively exploited in August, 2026 based on Recorded Future data (excluding honeypot-sourced CVEs).

Key trends: August 2026

• August reporting showed two AI-assisted operations: UAT-10147 exploited Zimbra, AjaxPro, Nacos, and Telerik servers before using DeepAudit and PentestGPT after compromise, while a separate Chinese-speaking actor weaponized Hermes Agent and DeepSeek in failed attempts against Langflow and n8n.
• 34 of the 73 vulnerabilities enabled remote code execution (RCE). They affected Microsoft productivity, database, server, and endpoint software; network edge and application delivery appliances; webmail, collaboration, content management, and web server platforms; AI, analytics, developer, and CI/CD services; and operational technology, product lifecycle management, file transfer, videoconferencing, and enterprise integration software.
• We identified public proof-of-concept (PoC) exploits and scanners for 53 of the 73 vulnerabilities.
• The most common weakness classes were CWE-94 (Code Injection) and CWE-502 (Deserialization of Untrusted Data) with seven each, followed by CWE-287 (Improper Authentication) and CWE-306 (Missing Authentication for Critical Function) with six each.
• 17 vulnerabilities were at least five years old, and the oldest was approximately 16 years old.

Trend analysis: AI-assisted workflows scale exploitation and target selection

Insikt Group detailed how Chinese-speaking threat group, UAT-10147, combined conventional exploitation with agentic artificial intelligence (AI) during post-compromise operations against internet-facing Windows and Linux web servers. Cisco Talos observed the threat actor exploiting or weaponizing CVE-2019-18935 in Telerik UI for ASP.NET AJAX, CVE-2021-23758 in AjaxPro, CVE-2021-29441 and CVE-2021-29442 in Nacos, and CVE-2022-27925 in Zimbra for initial access. After compromise, UAT-10147 used CVE-2010-3904, CVE-2015-3246, CVE-2015-5287, CVE-2021-3156, CVE-2022-0847, and CVE-2022-0995 for local privilege escalation on Linux.

Figure 1: Risk Rules history on the Vulnerability Intelligence Card® for CVE-2021-23758 in Recorded Future (Source: Recorded Future)

ASI continuously maps an organization’s external exposure, correlates newly surfaced vulnerabilities with real-world threat intelligence, and prioritizes response to enable defenders to remediate before adversaries can act.

This new function within ASI automatically creates signatures, pieces of detection logic that empowers the Recorded Future Platform to recognize a specific vulnerable or exposed condition across organization’s assets in real time.

With Automated Signature Creation now available, Recorded Future is helping to close the gap between AI-enabled threat discovery and enterprise defense.

Tackling the machine speed of exploitable vulnerabilities

It seems everything is moving quicker these days and the time to exploit a vulnerability is no different. A new generation of AI models is accelerating this challenge, demonstrating that they can automatically find zero-day vulnerabilities in major operating systems and web browsers — a skill that was previously exclusive to the most advanced government cyber units and research labs.

Back in 2020, we cited how Gartner confirmed that the time from discovery to exploitation dropped from 45 days to 15 days, between 2010 to 2020.

In our 2025 Malware and Vulnerability Trends report, we reported that weaponization occurred “within days of disclosure." Today, that window is measured in hours.

As a result, the status quo of traditional defenses and manual processes are no longer sufficient. Let’s look back at how we got here, from pre-existing detection methods to Recorded Future’s latest ASI enhancement to better defend against AI-accelerated vulnerabilities.

How we got here

In the past year, Recorded Future’s traditional approach of expert-authored signatures from the Insikt Group® was effective; they were high quality but moved at a human pace.

For example, in February 2025 we reported on the Trimble Cityworks: CVE-2025-0994, showcasing how manual signature creation worked. The Insikt Group built a Nuclei template (shared as a downloadable YAML file) specifically for CVE-2025-0994. This enabled defenders to test potentially vulnerable Trimble Cityworks instances prior to the patched version, serving as a detection and prioritization aid for helping teams figure out where to focus patching efforts first. This worked in conjunction with one of ASI’s core functions, scanning web infrastructure to identify internet-facing assets vulnerable to CVE-2025-0994.

Since that vulnerability disclosure a little over a year ago, we have ample evidence that the speed at which vulnerabilities are exploited has increased exponentially. Just recently, it was reported that OpenAI’s own agents went rogue and exploited a zero-day vulnerability in Artifactory, now infamously tied to the Hugging Face incident.

Incidents like this one, and the underlying vulnerabilities that facilitate them, are exactly why Recorded Future automated signature creation.

Now, in the face of an attack moving at machine speed, agentic processing generates production-ready detection signatures autonomously by turning a newly surfaced vulnerability into a deployable signature in as little as 31 minutes. As a result, the number of in-platform signatures produced has increased tenfold. Let’s take a closer look at how it works.

How automated signature creation works

So what does a signature in this context actually mean? Think of it like this: the signature is a piece of detection logic that says "go ask this asset this exact question; if the answer looks like this, it's vulnerable." It's the difference between "we found your assets" and "we found the ones a threat actor can potentially break into."

Automated signature creation works like a three-step early warning system. (See Figure 1)

1. The platform keeps a constant view of what your organization exposes to the internet such as domain records, certificates, and ownership data.
2. When a new vulnerability is flagged and matched against your scanned assets, it's checked against live threat activity rather than just a generic severity score. The system looks for evidence that threat actors are actually exploiting it, tying it to malware, ransomware or threat actor intent.
3. When Recorded Future Intelligence determines a CVE is relevant for detection, the system automatically processes it to generate a detection signature or product fingerprint in as little as 31 minutes.

Figure 1: CVE disclosures mapped to external assets kick off automated processes

H1 2026 activity showed a continued adversary preference for abusing legitimate tools, trusted platforms, and routine workflows already present in enterprise and consumer environments. Threat actors used exposed software, developer tools, remote access utilities, payment workflows, and third-party services to gain access, steal credentials, move laterally, and monetize intrusions while blending into expected activity. This emphasis on evasion through normalcy rather than technical novelty increases the risk that malicious activity will progress through approved tools and trusted services before defenders recognize it, reinforcing the need for stronger exposure management, identity and credential governance, behavioral detection, developer-environment security, backup resilience, mobile fraud monitoring, and third-party oversight.

AI-enabled cyberattacks became more visible in H1 2026, but remained mostly additive to established intrusion tradecraft. In the vulnerability ecosystem, AI-assisted research increased the volume of vulnerability reports, which, moving forward, could further compress remediation timelines by accelerating exploit-path analysis and lowering exploit-development costs for skilled operators. In malware activity, observed AI-enabled capabilities largely aligned with lower-to-mid-level stages of Recorded Future’s AI Malware Maturity Model (AIM3), where AI supported discrete functions such as persistence, user interface (UI) interaction, malware development, and delivery rather than fully autonomous operations.

Vulnerability exploitation broadened across enterprise operating systems, application frameworks, and network and security management products. Insikt Group identified 215 actively exploited common vulnerabilities and exposures (CVEs), and the most consequential cases combined network reachability, few access prerequisites, and code execution. Campaign reporting also showed that threat actors reused established post-exploitation playbooks across both newly disclosed and long-standing vulnerabilities, making exposure and impact more informative indicators of operational risk than vendor ranking or severity score alone. Across phishing and malware-delivery operations, threat actors repeatedly relied on familiar execution, obfuscation, discovery, and payload-transfer techniques rather than novel capabilities. Supply-chain compromises targeted package managers and developer environments, including AI-enabled tooling, where compromised credentials, trusted integrations, and software distribution channels enabled propagation into downstream cloud and software ecosystems. Other prominent activities included mobile malware enabling payment fraud through Near Field Communication (NFC) abuse and early AI-assisted workflows, as well as Magecart campaigns leveraging trusted third-party services and checkout manipulation.

The common risk across these threats is that malicious activity can progress through legitimate tools, trusted services, and routine workflows before defenders recognize it as part of a broader intrusion. Defenders should therefore prioritize vulnerabilities that can be exploited remotely or enable code execution, focus detection on suspicious sequences of behavior rather than isolated events, and strengthen controls protecting developer credentials, backup infrastructure, company-owned mobile devices, and payment environments.

Key Findings

• Insikt Group identified 215 actively exploited CVEs in H1 2026, up 34% from 161 in H1 2025. 142 of the 146 vulnerabilities that could be exploited without prior authentication were also network-accessible, and 60 of the 82 remote code execution (RCE) vulnerabilities combined network access with no authentication requirement.
• Similar to H1 2025, Recorded Future Malware Intelligence data showed the continued prominence of remote access trojans (RATs) throughout H1 2026. RATs led Insikt Group malware reporting, and Recorded Future Malware Intelligence submissions identified AsyncRAT as the top submitted malware family by total unique hashes and command-and-control (C2) configurations. AsyncRAT, Cobalt Strike, XWorm, Stealc, and REMCOS RAT remained in the top ten across H1 2025 and H1 2026.
• H1 2026 AI-enabled malware activity was concentrated in AIM3 Levels 1 to 3, with threat actors using AI to augment existing malware and intrusion workflows rather than conduct fully autonomous attacks.
• Ransomware operators continued to refine payload capabilities while relying on established intrusion methods, including ClickFix-style social engineering; exploitation of public-facing applications; abuse of legitimate tools such as s5cmd, PsExec, and AnyDesk; and techniques intended to reduce victims’ recovery options.
• Android NFC malware was the most notable mobile malware trend in H1 2026, with families such as NFCShare and NGate abusing device NFC functionality to steal payment card data, relay contactless transactions, and facilitate ATM cash-outs.

AI-Enabled Cyberattacks

AI-enabled cyber threats became more visible in H1 2026, but the available evidence indicates that most observed activity still augmented existing tradecraft rather than replacing it with fully autonomous operations.

In the vulnerability ecosystem, the release of Anthropic’s Claude Mythos Preview reinforced the growing relevance of frontier models to offensive and defensive security research. Broader vulnerability reporting also increased following the model’s release under Project Glasswing: June National Vulnerability Database (NVD) disclosures were 43% above the previous six-month average, while Mozilla reported that Mythos Preview identified 271 vulnerabilities that it fixed in Firefox 150, compared with 22 security flaws fixed following its earlier testing with Claude Opus 4.6. Vendors, AI developers, and bug-bounty platforms like Microsoft, Anthropic, and HackerOne have also reported rising vulnerability discovery or submission volumes alongside broader adoption of AI-assisted research, adding pressure to validation, disclosure, and remediation workflows.

Despite the increase in vulnerability reports, AI has not changed the fundamentals of vulnerability management: Attackers still need to identify, validate, weaponize, and operationalize vulnerabilities that offer reach, reliability, and return on investment. This means that only a small proportion of newly discovered vulnerabilities are likely to be a threat.

Nonetheless, AI-enabled vulnerability research can still increase defender workload in three ways: It can produce more credible vulnerability reports that require triage; reduce the time available to mitigate exploitable vulnerabilities by accelerating exploit-path analysis and weaponization; and lower the cost of exploit development by helping skilled operators produce proof-of-concept (PoC) code, test attack paths, and iterate toward weaponizable exploits more quickly. For defenders, the near-term issue is therefore not a sudden flood of fully autonomous exploitation, but a narrower window to determine which vulnerabilities matter most and remediate them before threat actors operationalize them. Additionally, early H2 2026 reporting on the July 2026 Hugging Face incident demonstrated that autonomous agents can perform discovery, validation, weaponization, and operationalization with limited human intervention. Defenders should prepare by automating vulnerability enrichment, prioritization, and mitigation to reduce the gap between machine-speed attack development and defensive response.

Figure 1: How automated capabilities will likely impact reporting, exploit development, and impact (Source: Recorded Future)

Malware reporting in H1 2026 showed more direct experimentation with AI-enabled capabilities compared to H1 2025. ESET identified PromptSpy as the first known Android malware to use generative AI (GenAI), using Google’s Gemini to interpret on-screen UI elements and generate step-by-step instructions to improve persistence across device layouts. Reporting on CANFAIL also showed threat actors using LLM-generated decoy logic or AI-assisted development artifacts to complicate analysis and facilitate malware delivery against Ukrainian organizations. These examples suggest that threat actors are testing AI in ways that can solve narrow operational problems: adapting to user interfaces, generating code or decoy logic, improving obfuscation, and supporting analyst confusion.

Threat actors also used AI-related products and ecosystems as delivery mechanisms. In February 2026, VirusTotal reported malicious OpenClaw skills disguised as useful automation for a local AI agent ecosystem, while Malwarebytes reported fake OpenClaw installers hosted on GitHub and surfaced through search results to deliver infostealers and proxy malware. This activity shows that attackers are exploiting user interest in AI tools in the same way they have historically abused popular software brands, distributing trojanized installers, malicious extensions, fake repositories, and dependency-based payloads through otherwise familiar channels.

As of H1 2026, Insikt Group assesses that most observed AI-enabled malware activity aligns with the low-to-mid-level stages of Recorded Future’s AI Malware Maturity Model (AIM3), specifically experimentation, adoption, and optimization, rather than fully autonomous AI-driven malware operations. Per the Recorded Future’s AIM3 framework, most publicly observed “AI malware” remains concentrated in Levels 1 to 3, where AI supports discrete operational tasks such as UI interpretation, persistence guidance, transaction timing, or operator guidance. Therefore, the near-term risk is not primarily self-directed malware operating independently, but rather the use of AI to make existing intrusion workflows faster, more convincing, more adaptive, and harder to analyze. Defenders should not expect a single control, model, or endpoint detection layer to reliably identify all AI-enabled malware activity. Instead, organizations should use defense-in-depth controls that can detect or disrupt different parts of the attack chain, including endpoint detection for script or installer activity tied to suspicious AI-themed downloads, extensions, repositories, or packages, mobile device management for accessibility service abuse and automated UI interaction, and restrictions on unapproved AI tools and installers.

Vulnerability Exploitation Trends

Key Takeaways

• Network reachability and low access requirements compounded risk, as 142 of the 215 exploited CVEs were network-accessible and could be exploited without prior authentication; 60 of those also enabled RCE, while public exploits were available for 66 (31%).
• Defenders faced both newly exploited flaws and persistent patch backlogs, as 162 CVEs were disclosed in 2025 or 2026, 53 predated 2025, and seventeen were dated from 2020 or earlier.
• Threat actors reused post-exploitation playbooks across different initial vulnerabilities; StrikeShark applied the same six-tool stack across thirteen CVEs, while Storm-1175 linked credential theft, remote execution, data transfer, and ransomware tooling across ten. More broadly, stealware was the most common malware category, followed by offensive security tools, backdoors, remote access trojans, and ransomware.

Microsoft Remains the Leading Vendor for Exploited Vulnerabilities

In H1 2026, Insikt Group identified 215 actively exploited vulnerabilities, up 34% from 161 in H1 2025. From January to June, the number of exploited vulnerabilities increased by an average of seven vulnerabilities per month. Microsoft remained the vendor most frequently associated with exploited vulnerabilities, accounting for 40 unique CVEs, up from 28 in H1 2025, a 43% year-over-year increase. Red Hat ranked second with fifteen CVEs, Cisco third with thirteen, Vercel fourth with eleven, and Fortinet fifth with nine. By comparison, H1 2025’s top affected vendors after Microsoft were Apple with eight CVEs, Ivanti with seven, Linux with six, and SonicWall, Google, Fortinet, and Craft CMS with four each.

The exploited vulnerabilities affected products from 98 vendors, 67 of which were associated with only one CVE. This indicates that exploitation was not confined to the most frequently affected vendors. For defenders, this supports maintaining risk-based remediation across the full software inventory, including less common products that may receive less monitoring or slower patching than widely deployed enterprise platforms.

At the product-family level, Windows and Windows Server accounted for the largest H1 2026 concentration, with twenty unique CVEs, followed by Red Hat Enterprise Linux with thirteen, Vercel Next.js with eleven, Cisco Catalyst SD-WAN Manager with eight, and Apple iOS and iPadOS with seven. While Microsoft exploitation remained prominent, H1 2026 activity also increasingly involved enterprise Linux, application frameworks, and network management products. Several vendors’ vulnerabilities were also driven by concentrated exploitation of one product family rather than uniform interest across the vendor’s portfolio: all eleven Vercel CVEs centered on Next.js, eight of Cisco’s thirteen affected Catalyst SD-WAN, and thirteen of Red Hat’s fifteen affected Red Hat Enterprise Linux.

Figure 2: Most affected vendors (top ten, including ties) by number of actively exploited vulnerabilities in H1 2026 (Source: Recorded Future)

For security teams, AI has generated both more excitement and more confusion than any technology in the last decade. As threat actors experiment with AI to hone their attacks, defenders are trying to determine which AI investments will help them measurably reduce risk.

Matthew Farmer, Accenture’s Managing Director of Security Operations in EMEA, joined Recorded Future’s co-founder Christopher Ahlberg and CTO and co-founder Staffan Truvé in a recent discussion to discuss the agentic SOC and what it takes to move from “AI theater” to real defense. Read on to see the key highlights from the discussion.

Avoiding the "productivity theater" trap

While AI is demonstrably transforming investigation and decision-making layers in SecOps, there’s a significant risk that organizations are falling into what Farmer calls "AI productivity theater."

"We can all agree that there's great production value around a lot of AI capabilities and AI products," he said. "But there are also organizations that are really struggling to achieve any kind of return on investment on their AI.”

The panel noted that the difference between success and failure doesn’t necessarily have anything to do with being in a regulated or non-regulated industry. It’s more about the ability to move past the theater by defining concrete KPIs.

“A lot of what people want to achieve with AI, we can already achieve with existing machine learning or SOAR automation capabilities,” Farmer said. So rather than simply deploying an AI solution for the sake of being AI-enabled, organizations need to ask whether they’re solving for cost improvement, risk reduction, or speed. They need to understand their KPIs so they can measure their true ROI.

Navigating technical and operational challenges

When it comes to bringing new AI solutions online, the panel noted that SOCs often face administrative, legal, and compliance limitations that eclipse any technical hurdles.

They also agreed that data quality and lack of context — “two sides of the same coin” according to Truvé — remain fundamental challenges.

Farmer noted that, “In the new world of tokenomics, it costs just as much money to troll through poor quality data as high-quality data.” It’s essential that security organizations feed only the best intelligence into their AI tools.

Assessing new risks, from democratization to agentic threats

Farmer said that security organizations used to ask a key question: “Do those [threat actors] with the capability have the motive, and do those with the motive have the capability?” We’re now in a world where non-capable threat actors can use AI to capably launch highly sophisticated attacks.

Threats are also becoming more structural. The panel highlighted "indirect prompt injection"—where agents are manipulated by the very instructions they read—as a new, distinct threat vector.

As companies deploy a digital workforce of AI agents, they should consider applying the same security principles of permissions, monitoring, and accountability to agents that they do to humans. But that may not be sufficient. "One big difference [between an agent and a human] is that an agent can spawn off a thousand clones of itself," Truvé said.

A critical challenge facing security teams is that the current observability space of SIEMs and traditional monitoring platforms isn’t built to track the internal state of an LLM.

"You can observe what ports they talk on, you can write that to a SIEM,” Ahlberg said. “But you’re not observing what’s happening inside the LLM.”

The panelists suggested that rather than relying solely on post-event observability, security teams should rethink how they control agents. Instead of setting up easily bypassed guardrails, security teams need to be better at constraining what each agent can do and ask for.

“You could imagine giving them a budget in terms of compute, communication, and delegation,” said Truvé. “These things run too fast. When you’re observing it, it’s already going to be too late.”

Preparing for the move to autonomous defense

According to the panelists, the shift toward autonomous defense is inevitable. "We can choose to go early, or we can choose to go late,” Farmer said. “But I think the decision is made for us."

However, it doesn’t need to take years to begin realizing big benefits from AI. To do so, security organizations should consider:

• Target high-friction areas, using AI to solve specific bottlenecks where addressable cost is low and ROI is immediate.
• Use outcome-based metrics, measuring success through model accuracy, escalation precision, and scan turnarounds rather than simple activity tracking.
• Assume breach, building defensive resilience that will pay dividends in the future.

On that last point, Farmer said he thinks that as teams grow more resilient, they develop a better appetite for deploying automated solutions — and that in turn strengthens their overall security posture.

The future of defense: Intelligence and speed at scale

According to the panel, the most profound change moving forward won’t just be the technology—it’ll be the velocity coupled with intelligence required for defense. "In three years, the main difference will be speed," Truvé predicted. "Defensive timelines will compress from days to minutes or seconds."

Ensuring security will require organizations to move past traditional constraints as they simply won't have time to manually ingest, analyze, and move intelligence. Taking detection engineering as an example, Farmer noted, “If we have to deliver more detection rules faster, we have to break that linear model between volume, speed, and headcount.” Consequently, SOCs will rely increasingly on high-quality, timely intelligence to enable rapid, automated decision-making.

As this shift occurs, the fundamental unit of work for a security analyst will evolve from handling individual alerts to managing and overseeing the agents that process them. In this new era, the human will remain essential—not as a manual processor of alerts, but as the architect who sets objectives, defines constraints, and monitors the behavior of the agents defending the enterprise.

Watch the full webinar here.

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Insikt Group has identified a series of BlueDelta initial access campaigns conducted between late September 2025 and early April 2026, targeting government and diplomatic organizations in Romania, Spain, and Türkiye. The campaigns delivered a lightweight Windows batch-script backdoor, dubbed "HOOKEDGE," via macro-enabled Microsoft Word documents using diplomatic-themed lures, including material impersonating Spain's Ministry of the Presidency, Justice and Relations with the Cortes, created shortly after a September 2025 meeting between Spanish and Moldovan officials.

Insikt Group assesses with moderate confidence that this activity was conducted by BlueDelta (which overlaps with APT28, Fancy Bear, and Forest Blizzard), a Russian state-sponsored threat group attributed to the Main Directorate of the General Staff of the Armed Forces of the Russian Federation (GRU). This assessment is based on significant code and tradecraft overlap between HOOKEDGE and the HEADLACE backdoor used in prior BlueDelta campaigns, consistent infrastructure patterns, and targeting consistent with known Russian intelligence collection priorities.

HOOKEDGE shares HEADLACE's core architecture, abusing legitimate webhook services for command-and-control (C2), payload staging, and data exfiltration, enabling malicious activity to blend with legitimate network traffic while reducing the operational overhead of dedicated infrastructure. The implant has undergone continuous refinement between September 2025 and April 2026, likely to evade automated sandbox environments and adapt to reduced free-tier API limits on webhook[.]site.

BlueDelta continues to invest in lightweight, easily adaptable initial-access tooling to support intelligence collection against European government and diplomatic targets. Rather than introducing new capabilities, the group has steadily refined its existing tradecraft, emphasizing operational resilience by adapting established tooling to evolving defensive measures and infrastructure constraints.

Organizations should prioritize blocking macro execution from internet-originated documents and implementing detection coverage for scheduled task abuse, headless Microsoft Edge execution, and outbound connections to webhook services.

Key Findings

• Between late September 2025 and early April 2026, BlueDelta conducted a series of initial access campaigns against defense manufacturing and diplomatic organizations in Romania, Spain, and Türkiye. BlueDelta used macro-enabled Word documents to deploy HOOKEDGE, a lightweight batch-script backdoor that shares significant code and tradecraft overlap with BlueDelta’s earlier implant, HEADLACE.
• The campaigns employed both diplomatic-themed and generic lures. Early activity impersonated Spanish government material, while later campaigns adopted generic macro-enablement lures. One diplomatic lure was created shortly after a meeting between Spanish and Moldovan officials, potentially reflecting an effort to collect intelligence relevant to Russia ahead of Moldova’s September 2025 parliamentary elections.
• BlueDelta continued to refine HOOKEDGE between September 2025 and April 2026, introducing changes to lure documents, execution methods, and beaconing intervals while maintaining the malware's core functionality and infrastructure model.
• For targets assessed as having higher intelligence value, BlueDelta deployed a second-stage HOOKEDGE payload with a much shorter beaconing interval. This gave operators more responsive tasking and follow-on activity, while keeping the webhook endpoints used for initial access from being exhausted.
• BlueDelta has historically demonstrated a preference for legitimate internet services (LIS) to facilitate C2, payload staging, and data exfiltration, with webhook[.]site’s free tier serving as the group’s exclusive choice across these campaigns.

Background

BlueDelta is a Russian state-sponsored threat group attributed to the Main Directorate of the General Staff of the Armed Forces of the Russian Federation (GRU), and overlaps with activity publicly tracked as APT28, Fancy Bear, and Forest Blizzard. The group has conducted espionage-focused cyber operations for more than a decade, consistently targeting government, diplomatic, defense, and policy-related organizations in support of Russian intelligence requirements.

BlueDelta has a well-documented history of obtaining initial access through spearphishing, credential theft, and lightweight custom tooling. Previous campaigns have employed malicious documents, Windows batch scripts, and living-off-the-land techniques (LotL), frequently abusing LIS and free web infrastructure for C2, payload staging, and data exfiltration. Insikt Group documented BlueDelta's 2023 use of the HEADLACE malware family, a lightweight, batch-based backdoor used during the early stages of intrusions to execute follow-on payloads and commands in initial access campaigns targeting organizations across Europe.

The activity detailed in this report represents a direct continuation of those operations. The HOOKEDGE backdoor shares significant code and tradecraft overlap with HEADLACE, demonstrating BlueDelta's continued preference for lightweight, easily modified malware that can be rapidly adapted to operational requirements. Similar activity has also been reported publicly by Lab52 under the name Operation MacroMaze.

Threat Analysis

Lure Documents and Targeting

BlueDelta's choice of lure documents across these campaigns reflects deliberate targeting of European diplomatic audiences and reveals collection priorities consistent with known Russian intelligence requirements. Between late September 2025 and early April 2026, Insikt Group identified several malicious macro-enabled Word documents likely used by BlueDelta in initial access campaigns targeting government or diplomatic personnel in multiple European countries.

The earliest identified lure, first seen on September 26, 2025, purported to be a September 15, 2025, meeting agenda from Spain’s Ministry of the Presidency, Justice and Relations with the Cortes, as shown in Figures 1 and 2. Although the document’s authenticity could not be verified, BlueDelta has historically used authentic government documents, including publicly available material, as phishing lures.

Figure 1: Screenshot of Spain’s Ministry of the Presidency, Justice, and Relations with the Cortes document (Source: Recorded Future)

Starting today, Recorded Future is launching AI Alert Filtering, an AI agent that automatically filters every Alert by relevance before an analyst opens it.

At scale, Alerts surface a lot of intelligence to work through, and the volume is only accelerating as threat actors are using AI to find vulnerabilities, spin up phishing infrastructure, and harvest credentials at a speed and scale that wasn't possible before. AI Alert Filtering turns that same AI advantage back on the problem, automating the first pass of Alert relevance so analysts spend their time on what actually warrants attention.

This gives analysts the benefit of seeing the highly relevant Alerts without giving up control. Customers with early access saw an average reduction in alert volume of around 63%, though results may vary based on rule configuration and use case.

Prioritizing intelligence at scale

Powered by Recorded Future AI, AI Alert Filtering takes on the first pass of prioritization, drawing on the Intelligence Graph® to classify references with the full context of Recorded Future's threat intelligence behind every call, not just the text of the reference itself. It sorts references by relevance, summarizes what came through, and explains its reasoning.

What we built

• High and Low Relevance sorting: Every reference inside a fired Alert is classified against the rule's intent. The High Relevance section loads first. Low Relevance items are still there if you need them; you're just not wading through them by default.
• AI summary at the top of every alert: Each Alert is delivered with a summary covering what came through, so analysts may quickly determine whether it demands immediate attention.
• Custom intent per rule: You can define exactly what the AI should prioritize, beyond the default intent Recorded Future ships with the rule. For example, "this is for ACME Bank, not ACME Center" sharpens results without rebuilding the rule from scratch.
• Optional auto-dismiss for empty alerts: When no references meet the relevance threshold, the Alert may be automatically dismissed instead of landing in your queue. Less to filter out, with the full details retained if you need to review it later.
• No data loss: AI Alert Filtering changes what gets surfaced, not what gets stored. The original, unfiltered Alert details are always available in the Portal.

Figure 1: Relevance sorting

Mexico’s Cybersecurity Plan 2025-2030: Turning Ambition Into Defense

Mexico has no shortage of cyber threats. Ransomware attacks are rising, criminal groups are exploiting stolen credentials and financial malware, and state-linked threat actors increasingly view the country’s government agencies, universities, and critical infrastructure as attractive targets. Mexico’s new National Cybersecurity Plan (hereinafter referred to as “Plan”), introduced in December 2025, recognizes many of these risks. However, it remains uncertain as to whether the government can build the institutions needed to address them proactively.

Mexico is ranked as a "Tier 2" nation in the ITU's 2024 Global Cybersecurity Index, placing it alongside Canada, Ecuador, and Uruguay in the upper ranks, trailing the United States (US) and Brazil, which have reached Tier 1 in the Americas. Despite that standing, Mexico is generally perceived by cyber experts as lagging behind international standards in institutional capacity-building, with international cooperation identified as an area requiring growth.

The question of whether the government can build the proper institutions has become more urgent in the aftermath of the FIFA World Cup 2026, which provided a high-profile stress test for Mexico’s digital defenses. With the tournament over and implementation of the government’s 2025-2030 cybersecurity plan beginning in earnest, Mexico faces a major opportunity to improve its cyber posture.

For this reason, the Plan represents a major opportunity for Mexican authorities to bring the country’s cyber readiness to the next level. Although there have been attempts to advance national cybersecurity policy, they have failed to gain traction. With this new Plan, President Claudia Sheinbaum's administration has committed to full implementation over the course of her term, aided by her party's majority control of Congress.

The Plan lays out a six-phase roadmap designed to gradually build Mexico’s cybersecurity capabilities through 2030, with later phases intended to deepen and institutionalize them.

• The 2025 Foundation Phase established a general framework for governance, risk management, incident reporting, and coordination, as well as initial steps to deepen international cooperation, including Mexico’s formal membership in the Latin America and Caribbean Cyber Competence Centre (LAC4) and a cybersecurity Memorandum of Understanding (MOU) with Brazil.
• The 2026 Expansion Phase, now underway, focuses on translating that framework into institutions through the passage of a new General Cybersecurity Law in Mexico, creation of a National Cybersecurity Operations Center, and integration of federal computer security incident response teams (CSIRTs).
• The 2027 Consolidation Phase would establish a National Cyber Range for red team and blue team exercises.
• The 2028 Maturation Phase would incorporate AI into cyber defense and develop a regional response center.
• The 2029 Leadership Phase aims to position Mexico as a cybersecurity services exporter across Latin America and the Caribbean.
• The 2030 Transformation Phase culminates in the establishment of a permanent Cybersecurity Observatory to track incidents, threats, and emerging technologies.

Threat Landscape

In a recent report, Insikt Group assessed Mexico's threat landscape across six persistent categories:

Ransomware is identified as the dominant threat. From January 2020 through April 2026, Insikt Group documented 223 ransomware incidents involving 64 groups and over 100 victims in Mexico. The top ransomware groups were LockBit, Qilin, CL0P, Kazu, and ALPHV (BlackCat), with government, manufacturing, information technology (IT), and food and beverage as the sectors most heavily impacted.

Insikt Group has identified several clusters of activity linked to PurpleDelta, Recorded Future's designation for North Korean IT workers, comprising multiple operators likely based in China. Between late 2024 and early 2025, one cluster applied to jobs at over 1,100 companies, primarily in the software and technology, staffing and consulting, and healthcare and biotechnology sectors. PurpleDelta operators maintained at least 22 fabricated personas across multiple clusters, some of which were supported by AI-generated profile photos, custom-configured ChatGPT assistants, and identity documents sourced from an illicit ID-generation service, and were highly likely to be actively employed by at least ten organizations.

PurpleDelta operators demonstrate a high operational tempo to this day. In some cases, the operators have applied to at least 60 positions per day across multiple job platforms, used multi-account management browsers and separate Google Chrome profiles to manage distinct personas simultaneously, and maintained detailed tracking spreadsheets to coordinate applications across identities. During job interviews, they used screen recording software alongside AI transcription and chatbot tools to generate real-time answers, often repeating ChatGPT responses verbatim. Once employed, operators recorded internal meetings at victim organizations and used Google Translate to draft pre-written excuses to justify using personal devices and bank accounts for work. Evidence from recorded sessions also indicates that PurpleDelta operators coordinated via Telegram and Slack, and at least two individuals were identified as facilitators who maintained company-issued hardware for the PurpleDelta operators.

Insikt Group assesses that this cluster of activity is consistent with the broader North Korean IT worker threat and presents material risk to organizations hiring for remote technical roles. Companies that have observed indicators listed in Appendix A should treat this as a potential active compromise and review the employment history and access privileges of matching individuals.

Key Findings

• Insikt Group has identified at least 22 fabricated personas linked to multiple PurpleDelta clusters that submitted applications to over 1,100 companies across the software, staffing, healthcare, and financial sectors, with operators submitting as many as 60 or more applications per day across at least 8 job platforms.
• These clusters of PurpleDelta operators are highly likely to have been actively employed at ten or more organizations, with confirmed or probable placements at companies that pose an ongoing and material insider threat.
• PurpleDelta demonstrated a high degree of operational sophistication, using multi-account management browsers, multiple Chrome profiles, AI-generated profile photos, custom ChatGPT assistants, and real-time AI transcription tools to deceive hiring managers during interviews, sometimes repeating AI-generated responses verbatim.
• Once employed, PurpleDelta operators recorded internal meetings at victim organizations, used screen recording software during work sessions, and drafted pre-written Google Translate excuses to justify the use of personal devices and personal bank accounts.
• Video evidence indicates that PurpleDelta operators use identity-brokering services, account-renting via AnyDesk, and multi-accounting tools, and coordinate via Telegram and Slack, with support from facilitators who procure and maintain company-issued hardware on the operators' behalf.

Background

PurpleDelta is Recorded Future's designation for the cluster of activity associated with North Korean IT workers, a state-directed network of covert technology laborers operating across global freelancing platforms and corporate hiring pipelines. The group overlaps with threat actor designations used by other vendors, including Jasper Sleet, UNC5267, Wagemole, and Famous Chollima. PurpleDelta operators pose as independent contractors and job-seeking developers to secure remote employment at organizations worldwide, with earnings systematically funneled through layers of individual facilitators, shell companies, and money-laundering front companies, ultimately financing the North Korean regime's sanctioned military and nuclear programs.

PurpleDelta operators employ extensive persona management tradecraft to obscure their nationality and true affiliation. Each operator maintains multiple fabricated identities across platforms, including GitHub, LinkedIn, Medium, Upwork, and a range of smaller freelancing sites, with personas deliberately constructed to project credibility through aged accounts, curated technology stacks, and cross-platform social proof. These identities are reinforced through the use of AI tools, temporary phone number services, anti-detect browsers, and resume-building platforms. In addition to generating illicit revenue, Insikt Group has observed signs of overlap with several North Korean state-sponsored groups, including PurpleBravo, a related cluster of activity that deploys malware through fraudulent recruitment campaigns targeting software developers primarily in the cryptocurrency space, indicating the broader potential for intelligence collection, downstream compromise, and supply-chain risk.

Threat Analysis

As part of Recorded Future’s ongoing tracking of PurpleDelta, Insikt Group has documented multiple clusters of North Korean IT workers since 2025 that are likely based in China. Operators in one of these clusters applied to jobs at over 1,100 companies. Almost half of the companies (~41%) to which the operators applied were in the IT and software services space, followed by staffing and consulting (~26%), and healthcare and biotechnology (~10%). Roughly 80% of the companies are based in North America, but the operators applied to companies in every region of the world. Many of the operators have a nexus in Shenyang, China, as indicated by their professional profiles, social media presence, and artifacts observed on their systems.

Figure 1: Breakdown of industries of the companies applied to by PurpleDelta operators (Source: Recorded Future)

Crypting services and products modify malicious payloads to help threat actors bypass detection, complicate analysis, and preserve malware usability after exposure. Although basic crypting consists of encrypting or obfuscating a customer-supplied payload, mature providers increasingly operate as broader malware-enablement services. Their offerings often combine payload wrapping, in-memory execution, anti-analysis checks, process injection, persistence options, delivery packaging, and post-detection “cleaning” or re-crypting services.

Insikt Group analyzed 24 threat actors advertising crypting services and products within the past year and identified a market that is competitive, reputation-driven, and heavily focused on Windows payloads. Providers advertise through underground forums, restricted communities, chat platforms, clearnet sites, and social media accounts. They compete through tiered pricing, antivirus (AV) detection scores of crypted samples, discounts, malware-developer partnerships, private or shared stubs, and promised turnaround times for re-crypting detected payloads.

Advertised crypter capabilities vary by provider, but the underlying objectives are consistent: reduce detection, delay or prevent analysis, and support stealthier payload execution. Because crypted payloads are designed to defeat both static and dynamic analysis, defenders should prioritize behavioral detection over static indicators. See the Outlook and Mitigations section for details.

Key Findings

• AV and endpoint detection and response (EDR) tools should not be treated as sufficient standalone protection against crypted payloads. Defenders should pair endpoint controls with behavioral detection, telemetry correlation, upstream hunting, suspicious process monitoring, and rapid triage of suspicious samples.
• Popular crypting service providers primarily advertise support for Windows payloads, with no identified advertising for macOS or Linux crypting services. However, although Windows environments were most frequently targeted by the services reviewed in this report, they are not inherently more susceptible to the execution of crypted payloads.
• Crypted payloads increase the likelihood of successful malware execution and delayed detection, but they do not independently provide end-to-end intrusion capability. Downstream activities, such as lateral movement, data theft, ransomware deployment, and follow-on compromise, depend on the embedded malware and the operator's objectives.
• Crypter risk varies significantly with provider maturity and technical capability: advanced crypters offer portability, anti-analysis, process injection, persistence, and security product bypass capabilities, whereas less-advanced crypters generally provide basic payload obfuscation techniques.
• Crypter capabilities are generally not novel individually, but their commercial packaging makes established defense-evasion tradecraft easier to access, reuse, and operationalize. The significance of crypters lies less in technical innovation than in making mature evasion methods available as paid services.

The Crypter Landscape

What Is a “Crypter”?

“Crypting” is what threat researchers generally refer to as a service or product wherein a file, almost exclusively a malicious executable of some kind, is encrypted to bypass malware detection technologies. The result of a crypting service is a malicious payload that modifies the supplied executable in ways that deter defenders and endpoint security solutions (namely, AV and EDR products) from detecting and analyzing it.

How Does Crypting Work?

While the core functionality of a crypting service or product is to encrypt a payload, services vary in the capabilities they provide. These capabilities can range from the encryption algorithms used, which are often proprietary, to behavioral adjustments for how the resultant payload will execute in a victim environment. For instance, many crypting services include execution guardrails or methods for indicator suppression, such as ensuring execution fails in virtual environments or performing environmental scanning before execution to determine whether the payload is running in an analysis or sandbox environment.

By using crypting services, threat actors can evade detection and prevent their malicious code from being easily exposed to researchers, an important consideration for those who wish to reuse payloads for extended periods without detection.

Who Is Selling Crypting Services?

The crypter landscape comprises a community of criminal threat actors that often operate on restricted or closed networks, including the dark web and underground forums, to market and sell their crypting services. Threat actors may also opt to market their services on clearnet websites they own and operate. In some instances, threat actors may be accessible only via messaging platforms, such as Telegram or TOX, which likely mask their exposure and reduce the likelihood of sensitive data leaking through forum chats. Finally, some threat actors have created social media accounts where they post updates on their services, partnerships, pricing, and links through which interested buyers can inquire about purchases.

Additionally, partnerships between malware developers and crypting service providers are not uncommon. For instance, a well-established crypting service provider on underground forums, “GoldenCrypt”, is reportedly affiliated (1, 2, 3) with multiple malware families, including FvncBot, Albiriox, and Mirax. The level of affiliation between a crypting service provider and a malware developer can range widely, from providers with loose reputational ties to developers who are mainly affiliated with one hacking group and will opportunistically provide services to third parties. However, these partnerships are often a marketing strategy that crypting service providers use to secure kickbacks and boost positive reputational sentiment.

All of these services come at widely varying costs, typically based on core factors. For instance, crypting service fees are often tied to the volume and types of files to be encrypted, as well as the duration of service, with almost all crypting service providers offering tiered payment options along these lines. The actual prices of these tiers are pegged to additional factors related to the provider and their product, including the reputation of the threat actor, the capabilities of their encryption service, the promise that a crypted payload is fully undetectable (FUD), and additional features provided to the buyer. Threat actors will support the assertion that their payloads are FUD by using multi-AV platforms, the most common of which is KleenScan, a service that allows threat actors to scan samples without storing and potentially exposing the samples to researchers. As with other legitimate services, service providers also advertise discounts and similar deals to remain competitive with their peers.

Who Is Using Crypting Services?

Many threat actors have been observed in the wild using crypting services due to their stealth capabilities. All malware types can be crypted, and the key factors determining whether a threat actor can use a crypting service are the targeted device’s operating system and the payload’s programming language. For instance, the most common crypting services are geared toward Windows .exe and .dll payloads. Still, considerations such as whether a payload is coded in .NET, C, or C++ can provide additional capabilities for crypted payloads or, in other instances, prevent certain payloads from being crypted.

While the use of crypting services is common among threat actors, it is by no means ubiquitous. Due to sometimes prohibitive pricing strategies and an environment that often relies on reputational checks before purchase, crypting services are often used only by well-established criminal threat actors or larger threat actor groups that have their own specialist or custom tooling for crypting payloads. Furthermore, as discussed previously in this report, not all crypting services support all types of payloads. While this does not expressly ensure that a payload cannot be crypted, threat actors seeking to crypt more unique types of executables may find their options limited or even nonexistent.

Nevertheless, crypting services are commonly marketed by threat actors and are commonly implemented by professional threat actor groups. Multiple open-source reports on high-impact cyberattack campaigns have supported this. For instance, in July 2025, eSentire reported an association between PureRAT, a remote access trojan (RAT) first advertised in January 2023, and GhostCrypt, a crypting service sold by an underground forum member of the same moniker, in an attack that impacted a public US accounting firm in May 2025.

Crypting Service Characteristics

Insikt Group investigated 24 crypting service or product providers active within the past year and identified the various capabilities advertised by each. This information helped determine the capabilities most commonly displayed by crypting services, products, and the payloads they create, and provided insight into the capabilities most desirable to customers buying these services and products. A heat map of the techniques advertised by these threat actors is shown in Figure 1, below. (The list of MITRE ATT&CK techniques shown in Figure 1 can also be found in Appendix B.)

Figure 1: MITRE ATT&CK Enterprise tactics and techniques described in crypting advertisements (Source: Recorded Future)

The agent discovered and exploited previously unknown vulnerabilities. The more consequential development came afterward. Over a four-and-a-half-day campaign, it carried out roughly 17,600 actions against Hugging Face’s infrastructure. Most of those actions failed. The operation advanced because each failure imposed little cost, and the next attempt could begin immediately. The system could keep exploring, reconstruct its tools, revisit abandoned paths, and test another hypothesis without fatigue or meaningful opportunity cost.

That changes both the economics and the tempo of cyber offense.

For most of cybersecurity history, sustained intrusion activity has been constrained by human attention. Skilled operators have limited time, and every unproductive hour spent on one target is unavailable for other work. AI erodes that constraint. The significance of 17,600 actions lies as much in their concentration as in their cost. They occurred within four and a half days.

AI enables concentration of effort in time. A motivated attacker has always been able to spend more on a valuable target, but money alone could not make human operators sustain this tempo. The new advantage is the ability to concentrate thousands of attempts on a single environment before a defender’s traditional processes can assemble the evidence.

The future threat is therefore unlikely to resemble a single, infallible artificial hacker. It is more likely to resemble a flood of low-confidence activity probing the accumulated technical debt of modern enterprises.

Large organizations are especially fertile terrain. Their environments have developed over decades, layering legacy systems with cloud services and inherited trust relationships that no one designed as a whole. An autonomous system need not outperform the best human attacker. It only needs to search for that complexity cheaply and quickly enough to find the few combinations that work before defenders can assemble the pattern.

The OpenAI incident occurred under highly unusual conditions. The models were being evaluated for advanced cyber capabilities with reduced refusals, substantial inference resources, and an objective that encouraged persistent exploration. OpenAI has also said that the most capable research prototype involved was never intended for public release. Its complete technical investigation remains unfinished.

Those caveats should temper sensationalism. They do not invalidate the warning.

Frontier capabilities diffuse. Models become smaller, cheaper, and easier to operate. Techniques developed in advanced laboratories eventually migrate into commercial tools, open-source projects, state programs, and criminal services. Recorded Future’s research had previously found that complex autonomous cyber operations remained beyond the practical capabilities of generally available models at the time, while the broader trajectory pointed toward more complex agentic operations. The Hugging Face incident suggests that this transition is occurring faster than many defenders expected.

The question is no longer whether AI can execute a multistage intrusion under the right conditions. It is how quickly those conditions will become cheaper to reproduce.

Layered defense for the agentic era.

Prevention remains essential, yet this incident also exposes its limits.

The first breakthrough did not occur within Hugging Face’s perimeter but stemmed from a previously unknown vulnerability in an Artifactory component (CVE-2026-65617, CVE-2026-65923, and CVE-2026-66018) in OpenAI’s evaluation environment. Before the campaign reached Hugging Face, neither Hugging Face nor an external intelligence provider had a factual basis to know that this specific path existed or that Hugging Face would become the target.

If agentic systems increase the frequency with which unknown vulnerabilities are discovered and exploited, organizations must assume that some first lines of defense will fail without warning. The stronger defensive question is how many independent opportunities remain for detecting and constraining what follows.

The more useful objective is to prevent one success from becoming twenty.

The Hugging Face compromise advanced because the agent could convert access in one environment into authority elsewhere. A compromised workload exposed additional secrets. Those secrets opened new trust relationships. Each successful transition gave the agent more information and more privilege with which to attempt the next one. Consequently, Hugging Face’s remediation focused on reducing that trust inheritance by narrowing credential scopes, strengthening workload identities, increasing isolation, and implementing more restrictive access controls.

Layered defense is not simply a matter of adding more controls. Each boundary should limit the authority inherited from the previous step and produce evidence when that boundary is crossed.

A resilient environment forces the attacker to solve a genuinely new problem at every stage. A fragile environment allows one credential or trusted connection to collapse several boundaries at once. AI makes that distinction more consequential because an autonomous system can exhaust permissive paths faster than a human team can understand their cumulative significance.

Least privilege remains essential, but architecture alone will not settle the contest. Large enterprises cannot eliminate complexity. Their environments continue evolving across legacy and cloud systems, accumulating exceptions and trust relationships that appear reasonable in isolation.

Layered defense must therefore combine structural separation with the ability to recognize when an attacker is assembling those isolated exceptions into a route through the enterprise.

A mature environment makes every additional move more expensive for the attacker and more legible to the defender.

The critical metric is time to conviction.

Hugging Face’s most important disclosure concerns the period after its controls began producing evidence.

The company reported that multiple security systems observed anomalous activity. Its AI security stack correlated those events into a coherent signal, yet the finding was not judged sufficiently critical to wake the response team in time.

Hugging Face collected much of the evidence it needed. The breakdown occurred in assessment and escalation. Available signals were not converted into a judgment urgent enough to trigger action.

Most mature organizations already produce more alerts than their teams can investigate. Their scarcer resource is certainty. An unusual authentication or an unfamiliar public service may be benign when viewed in isolation. The campaign advances while the defender tries to determine whether the observations are connected.

When offensive effort is compressed into a few days, the defender cannot afford for context to arrive one console at a time. A delay of hours can be enough for the attacker to cross the next trust boundary.

Traditional security operations evaluate discrete alerts after they occur. Each tool asks whether its own event appears malicious, and analysts later reconstruct the wider campaign. That model is static and retrospective. Time disappears as evidence moves between systems, is repeatedly interpreted, and is reassembled by people who may see only one portion of the environment.

The defensive system of the future must instead maintain a living hypothesis. Every new observation should update the probability that a campaign is underway. Yesterday’s suspicious use of the token may initially carry little weight. When today’s unfamiliar network destination appears, the system should reconsider both events as part of the same investigation.

The unit of defensive work becomes the evolving campaign rather than the isolated alert.

This is where intelligence has to become operational.

For years, threat intelligence was treated largely as external knowledge delivered into a security program. That model remains useful, but it can be incomplete against an adversary that can generate new infrastructure faster than defenders can assign reputation to it.

Attackers have long abused legitimate public services and disposable infrastructure. Agentic AI did not create that tactic, but it increases the speed and volume at which the tactic can be used. A static list of malicious infrastructure ages faster when a system can discard one endpoint and establish another without human delay.

The meaning lies in the relationship between those services and the behavior occurring within the victim’s environment.

Intelligence in the agentic era must provide that connective tissue. It must combine what the outside world knows with what the organization itself is observing, then preserve and revise that assessment as the operation changes.

That is the underlying premise of the Intelligence Graph® at Recorded Future. Its value comes from preserving relationships across time, not simply from containing a large volume of information. Autonomous Threat Operations applies that context to continuous investigations across the controls a customer already has. It does not replace those controls or the analysts operating them. It can help prevent an investigation from losing its accumulated context whenever the attacker changes technique or the evidence moves into another system.

No counterfactual can guarantee that this would have prevented the Hugging Face incident. The defensible claim is narrower.

Once observable activity began, a customer with the relevant telemetry and integrations could have defended differently. Persistent hunts might have linked unusual credential behavior to the compromised workload without waiting for analysts to manually reconstruct the context. External intelligence could have helped distinguish ordinary use of public infrastructure from a rapidly changing command channel. New evidence could have revised an existing investigation rather than creating another isolated queue of alerts.

Intelligence could not have predicted the first private zero-day. It could have created more opportunities to interrupt the operation before the agent accumulated durable privilege.

Defensive autonomy requires different constraints.

The natural response to autonomous attack systems is to demand equally autonomous defenders. Defensive autonomy, however, operates under a different set of constraints.

An offensive system can test thousands of unsuccessful paths without harming its own operation. Defensive action has consequences for the business it is intended to protect. Indiscriminate blocking can disrupt legitimate activity and create an operational incident in its own right.

Automated systems are best suited to work where delay is expensive, and the consequences of error are limited or reversible. They can maintain investigations continuously, correlate new evidence, and take bounded actions under predefined conditions. Human judgment should remain concentrated on decisions that could materially disrupt the business.

Organizations should gradually expand the scope of automated defensive actions. The progression should begin with observation and explanation, then move toward low-risk and reversible actions as performance becomes measurable. More consequential authority should remain governed by explicit technical and organizational guardrails. Those guardrails cannot be static. Human oversight must remain in the loop to test whether defensive agents are focused on the right threats and behaving as expected. The human role is not limited to approving a consequential action; it includes governing the system as its assumptions and behavior change over time.

Recorded Future has taken this approach with Autonomous Threat Operations, which supports continuous hunting and multi-source correlation while allowing customers to govern how intelligence is operationalized.

The distinction between automation and autonomy also matters. An automated rule repeats a predetermined response. An autonomous system revises its investigation as the evidence changes. The Hugging Face agent altered its methods when previous paths failed. A defense based entirely on fixed workflows will struggle to maintain pace with that adaptation.

Defensive systems do not need to mirror every attacker's action in real time. They need to preserve continuity of understanding while the attacker moves.

Human analysts remain essential. Their future value will lie less in moving indicators between products than in challenging the system’s conclusions and owning decisions that cannot be easily reversed.

They should govern the defense both at the moment of action and over the loop that produces it. Moving context manually between tools is work the system should absorb.

Connected intelligence becomes more valuable as models commoditize.

The models available to attackers and defenders will continue improving. Over time, access to competent cyber agents will become less distinctive. A model advantage that appears significant today may disappear with the next release or open-source replication.

Individual data sources may also be commoditized. Agents will make collection cheaper, and more companies will possess useful but partial views of risk. The durable advantage lies in quickly assembling those fragments into a coherent picture that can change a decision.

An attacker can begin each operation with a new model instance, fresh infrastructure, and no durable identity. That can make attribution more difficult. The defender’s advantage lies in continuity: years of knowledge about its own environment, joined with external intelligence and signals from the wider economy.

That advantage is often wasted because the evidence is partitioned by domain. One system can see the cyber compromise while another sees downstream abuse, yet no layer assembles them quickly enough to maintain the whole argument.

The strategic role of intelligence is to make that accumulated knowledge usable at the moment of decision.

This is where the combination of Recorded Future and Mastercard becomes distinctive. Recorded Future helps connect weak cyber signals across the Intelligence Graph and sustain the investigation as those signals change. Mastercard adds fraud expertise and payment-risk signals that can reveal how compromise is beginning to manifest beyond the victim’s network. The advantage lies in assembling those perspectives early enough to interrupt the operation.

Vulnerability Prioritization becomes relevant when a private flaw begins to produce public evidence, allowing defenders to understand whether the issue is moving from theoretical exposure to operational exploitation. Attack Surface Intelligence determines where the vulnerable technology intersects with the organization. Digital Risk Protection can warn when credentials have been exposed externally. Third-Party Risk helps determine whether a supplier’s incident changes the customer’s own exposure.

These capabilities matter most when they inform one another. Their purpose is to produce one defensible judgment about what the organization should do next.

This is where defenders can build a genuine asymmetry.

Offensive systems can be disposable. Connected defensive intelligence can compound. Each investigation adds context to the next, and each new source can strengthen or challenge the current assessment. An organization that can assemble those perspectives in time forces the attacker to overcome both today’s controls and the accumulated lessons of previous attempts.

The decisive advantage will be temporal.

The Hugging Face incident does not prove that autonomous cybercrime has arrived at scale.

As capable models become cheaper, attackers will be able to sustain more simultaneous attempts. The first effect may be volume rather than brilliance. That alone changes the equation.

Organizations cannot answer this shift simply by producing more alerts or placing a human analyst in the middle of every decision. Prevention will remain essential, but some first controls will inevitably fail.

They will need layered architectures that limit how far one success can travel. They will need intelligence that preserves context while the attacker changes shape. They will need an autonomous investigation whose authority remains bound by the consequences of getting a decision wrong.

Two opposing curves will determine the future of cyber defense.

For the attacker, the cost of another attempt is falling, while the number of attempts that can be concentrated within a single operational window is rising.

For the defender, the time required to assemble weak signals into a coherent judgment must fall faster.

Recorded Future’s role in that future is practical: connecting weak signals across a broad Intelligence Graph and sustaining the investigation as those signals change, so decision-makers gain conviction before temporary access becomes enduring control.

Foreknowledge of every private zero-day is impossible. Continuity after the first observable signal is achievable.

AI is making persistence cheap. The defenders who prevail will make progress expensive.

The 85 vulnerabilities in this report affected products from 61 vendors, with Microsoft accounting for approximately 12% of the vulnerabilities. The remaining exposure was concentrated across a range of enterprise software, security products, network infrastructure, developer tooling, and cloud platform vendors.

Insikt Group previously created a Nuclei template to detect the Langflow vulnerability (CVE-2025-3248) featured in this report. These are available to Recorded Future customers via the Recorded Future Intelligence Platform.

Quick reference: July 2026 Vulnerability Table

All 81 vulnerabilities below were actively exploited or operationally weaponized in July 2026. This table does not include the four CVEs that were primarily surfaced through our honeypot data, which are available to Recorded Future Intelligence Platform customers via the CVE Monthly report. The table below also provides examples of public PoCs identified by Insikt Group. These PoCs were not tested for accuracy or efficacy. Vulnerability management teams should exercise caution and verify the validity of PoCs before testing.

Table 1: List of vulnerabilities that were actively exploited in July, 2026 based on Recorded Future data (excluding honeypot-sourced CVEs).

Key trends: July 2026

• In July 2026, the Dysphoria botnet was used to exploit known IoT and embedded-device flaws to build DDoS and relay infrastructure; Cloud Atlas abused Microsoft Equation Editor to deliver CloudAtlasGo; Armored Likho used a malicious Windows shortcut to deploy BusySnake Stealer; and JADEPUFFER and Cl0p targeted exposed AI and product-lifecycle platforms for encryption, data theft, and extortion.
• 57 of the 85 vulnerabilities enabled remote code execution (RCE), including flaws affecting Microsoft, Fortinet, Langflow, ServiceNow, WordPress, and Joomla ecosystems, internet-facing security appliances, and embedded network devices.
• We identified public proof-of-concept (PoC) exploits and scanners for 60 of the 85 vulnerabilities in this report.
• The most commonly observed weakness classes were CWE-78 (OS Command Injection), CWE-434 (Unrestricted Upload of File with Dangerous Type), CWE-94 (Code Injection), and CWE-502 (Deserialization of Untrusted Data).
• 14 of the 85 vulnerabilities in this month’s table are at least 5 years old, with the oldest approximately 18 years old, reinforcing how threat actors continue to exploit long-known weaknesses in environments where patching has lagged. Additionally, the fastest observed time from a vulnerability’s public disclosure to reported exploitation was less than one day.

Trend analysis: Malware-Linked Exploitation Spans IoT, Email, and Enterprise Applications

An Insikt Group® TTP Instance on the Dysphoria botnet linked CVE-2013-3307, CVE-2016-20016, CVE-2017-17215, CVE-2017-5259, CVE-2018-14558, CVE-2020-25499, CVE-2020-8515, CVE-2022-35733, CVE-2025-28137, CVE-2025-34152, CVE-2025-55182, CVE-2025-9528 to the exploitation of routers, gateways, cameras, repeaters, and other embedded Linux devices. Dysphoria combined known RCE flaws with weak Telnet and Secure Shell credentials to enroll compromised systems into DDoS and relay infrastructure.

Figure 1: Vulnerability Intelligence Card® for CVE-2017-17215 in Recorded Future (Source: Recorded Future)

Neurotechnology is rapidly moving beyond clinical use cases, expanding the attack surface for sensitive neurological and biometric data: As adoption grows, larger volumes of brain activity, biometric, and behavioral data will be collected by commercial platforms, creating new opportunities for data theft, misuse, and exploitation.

China and the United States (US) are engaged in strategic competition in neurotechnology development: The US leads in the number of neurotechnology firms, and brain-computer interface (BCI) research has been a long-term research priority for the US military. At the same time, China’s five-year guidance for BCI development, subsidies for major wearable technology firms, and military research into human-machine integration suggest that neurotechnology is a strategic priority.

Leading neurotechnology companies are likely to face increased targeting for intellectual property (IP) theft: Because neurotechnology is costly to develop and strategically valuable, companies in this sector are likely to become attractive targets for state-sponsored espionage, insider threats, and cyber-enabled theft. Successful IP theft could erode the competitive advantage of companies that invest heavily in research and development (R&D). Military and higher education research laboratories are also likely to be targeted for access to R&D and related data.

Neurological and biometric data will become an increasingly valuable target for cybercriminals and state-linked actors: Attackers may seek to exfiltrate these datasets for extortion, surveillance, strategic intelligence, or model development. The sensitivity of this data could make breaches particularly damaging for affected individuals and companies, making it an attractive target for extortion-focused cybercriminals.

Regulatory and national security scrutiny of neurological data will likely intensify: Existing privacy frameworks in the European Union (EU) and several US states already provide heightened protections for neurological or biometric data, but rapid advances in neurotechnology may outpace consumer protection laws.

Figure 1: Key threats in neurotechnology and how they will evolve (Source: Recorded Future)

What Happened

The incident began during an OpenAI evaluation designed to measure advanced cyber capabilities. The evaluation involved GPT-5.6 Sol and an even more capable internal research prototype that OpenAI says was not intended for public release. Because the objective was to test their maximum offensive potential, the models were operated with reduced security guardrails (normally used to prevent high-risk activity).

According to OpenAI, the models spent substantial computing resources searching for a path to the internet. They exploited a zero-day vulnerability in Artifactory, the package-registry cache proxy available in the evaluation environment, and then performed privilege escalation and lateral movement within OpenAI’s research environment until they reached a node with internet access.

Once online, the models inferred that Hugging Face might contain models, datasets, or solutions associated with ExploitGym, the benchmark they had been instructed to solve. OpenAI says the agents then chained stolen credentials, zero-day vulnerabilities, and remote code execution paths to obtain test solutions from a Hugging Face production database.

Hugging Face’s technical reconstruction covers roughly 17,600 recovered agent actions, grouped into approximately 6,280 clusters, between July 9 and July 13, 2026. Although most attempts failed, the agent still gained administrative access to internal and production systems, accessed some source-code repositories, and used a stolen credential to connect external systems to the company network. Hugging Face found no evidence that its public models, datasets, services, packages, or published software supply chain were altered. It says the only customer content accessed comprised five datasets, apparently related to ExploitGym or CyberGym.

A Capability Breakthrough and a Control Failure

Most coverage of this incident has framed it as a breakthrough in model capabilities, since it is the first known instance of a model apparently independently conducting an end-to-end cyberattack. The underlying techniques were largely familiar to those used in human-led cyberattacks: vulnerability exploitation, credential theft, staging, remote code execution, lateral movement, and abuse of privileged or trusted access. What was notable was the agents’ ability to select and chain those steps autonomously across a long-running operation. This represents a significant shift in the speed and scale of future incidents.

An autonomous agent can execute thousands of actions, test multiple paths, and continue working without the fatigue, coordination costs, or time constraints that limit human operators. In this case, most of the roughly 17,600 recovered actions were associated with failed paths. The operation was noisy and failure-prone, but it needed only a small number of successful chains to create a material impact.

This compresses what could be called time to everything:

• Time from vulnerability discovery to exploitation
• Time from initial access to privilege escalation
• Time from data theft to analysis and extortion
• Time from defensive signal to attacker adaptation

What this means for defenders is that any exposed flaw may now have disastrous consequences in the era of agentic attackers. The agents exploited multiple errors across the Hugging Face environment to carry out the attack, finding these weak points and exploiting them much faster than a human operator. The new pace of operations makes continuous vulnerability management, threat visibility, and risk mitigation essential to limiting the impact of an agentic attack.

The Greater Risk May Be Your Own Agents

While the capability breakthroughs are noteworthy, the incident is also a failure of AI safety. Removing behavioral safeguards may have been necessary to measure the models’ maximum capabilities, but doing so should have increased reliance on compensating controls: strict network isolation, credential separation, resource limits, real-time telemetry, and automatic termination when predefined boundaries were crossed.

Instead, an evaluation intended to simulate advanced cyber activity became a real security incident affecting an external organization. This should be a warning to any organization deploying AI agents: without strict AI governance, agents can act outside their authorized scope while still pursuing the objective they were given.

An enterprise agent does not need malicious intent to cause harm. A poorly scoped objective, excessive permissions, and inadequate operational boundaries may be enough.

An agent instructed to “resolve a customer issue,” “complete the research,” or “fix the vulnerability” may decide that the most efficient path involves accessing restricted information, interacting with an external system, accepting contractual terms, modifying production code, or using credentials in a manner its operator never anticipated. Any of these could potentially result in a major security incident.

The fact that an agent selected an action does not transfer accountability away from the organization that deployed it. Depending on the facts and applicable law, an organization may face legal, contractual, or regulatory exposure based not only on how information is used, but on how the agent obtained it.

The Executive Agenda

The Hugging Face incident shows that using agents for defense will be essential to counter agentic offense, but must be approached with a thorough understanding of the risks.

Govern authority and ensure checks on higher privileges. Every agent should have a clearly defined identity, narrowly scoped permissions, and explicit limits on activity. In cases where agents need higher levels of permissions, mitigating controls such as approval gates or additional monitoring (detailed below) should be applied.

Design containment on the assumption that behavioral safeguards may fail or be reduced. Prompts, refusals, and policy controls should be treated as one layer of protection — not as the security boundary. Network segmentation, egress controls, credential isolation, and execution limits must remain effective even when a model behaves unexpectedly.

Create approval gates for consequential actions. Transactions, production changes, external communications, credential use, and access to sensitive systems should require deterministic policy checks or human authorization.

Monitor behavior, not just outputs. Organizations need visibility into the tools an agent invokes, the systems it contacts, the credentials it uses, the resources it consumes, and the sequence of decisions leading to an action. Unusual compute consumption or repeated attempts to bypass a boundary should trigger intervention.

Prepare for machine-speed defense. Security programs will need automated enrichment and prioritization to determine which of a rapidly expanding set of vulnerabilities and attack signals create genuine exposure. In addition, automated response and mitigation are necessary to successfully defend against automated attackers.

Two Possible Futures

The long-term impact of agentic AI will depend largely on two variables: how capable the systems become and how broadly access to those capabilities is distributed.

Scenario One: Capabilities Proliferate

In this future, highly capable agents remain broadly available through commercial services, open-weight releases, and illicitly modified models.

Attackers use these agents to automate reconnaissance, vulnerability discovery, social engineering, and lateral movement. Defenders will need to apply these capabilities to automated response and mitigation, producing an accelerated contest between machine-speed offense and machine-speed defense.

The advantage is unlikely to be distributed equally. Large technology and security companies will be better positioned to deploy sophisticated defensive agents, operate extensive telemetry networks, and rapidly isolate compromised systems.

Smaller organizations may increasingly depend on managed platforms and tightly controlled technology ecosystems. The result could be an internet composed of more walled gardens, as organizations gravitate toward providers capable of operating security at machine speed.

Scenario Two: Frontier Access Narrows

In the second future, governments and model providers restrict access to the most capable systems through trusted-partner programs, identity requirements, geographic limitations, or export controls.

In June 2026, a White House executive order directed federal agencies to design a voluntary framework for evaluating “covered frontier models.” The same month, the Commerce Department temporarily applied export controls to Anthropic’s Mythos and Fable models, requiring restrictions on access by foreign nationals — an unprecedented move to regulate access to AI models themselves rather than only to advanced chips or model weights.

Chinese authorities have reportedly considered similar restrictions on foreign access to the country’s most advanced models, although no final policy had been announced at the time of reporting.

This future would concentrate frontier capabilities among governments, critical infrastructure operators, and large, approved organizations. Other businesses would rely on older commercial models, open-weight alternatives, or systems that can be modified to bypass safeguards.

Restrictions could reduce broad access to the most dangerous capabilities, but they would not eliminate malicious use of AI. Criminal groups would continue to jailbreak available systems, steal model access, and use open-weight alternatives. Enterprises could also face sudden operational disruption if a model embedded in a critical workflow becomes unavailable due to regulatory changes, vendor policies, or geopolitical tensions.

The Most Likely Future Is a Mix of Both

These scenarios are not mutually exclusive.

The more probable future is a fragmented ecosystem in which the most advanced models are increasingly gated, while slightly less capable open-weight systems remain widely available. Criminals, states, major technology companies, and ordinary enterprises will operate at different levels of capability and under different constraints.

This means organizations cannot base their security strategy on the assumption that governments will successfully contain dangerous models — or that unrestricted access will continue indefinitely.

They must be prepared for both capability proliferation and access disruption.

The Hugging Face incident does not show that AI systems developed human-like intent or an independent, malicious objective. Nor was it merely a harmless laboratory accident.

It is evidence that autonomous systems can turn narrow instructions into consequential external actions outside their operators’ intended scope, using familiar weaknesses at a speed and scale that existing controls may not withstand.

The central question for executives now is how to manage the authority those agents should receive and how to mitigate the likelihood and consequences of their pursuing objectives outside authorized boundaries.

Read more in the “AI Hype vs. Reality” series:

• The AI Malware Maturity Gap
• Why The Vulnerability Backlog Is About to Get Worse

About Insikt Group^®

Recorded Future’s Insikt Group, the company’s threat research division, comprises analysts and security researchers with deep government, law enforcement, military, and intelligence agency experience. Its mission is to produce intelligence that reduces risk for customers, enables tangible outcomes, and prevents business disruption.

AI is changing the threat landscape faster than most security organizations can keep up. Recorded Future co-founder Christopher Ahlberg, CTO and co-founder Staffan Truvé, and Head of Threat Intelligence Levi Gundert unpack what’s actually happening in a recent conversation — and what it means for your defenses. Read on for their 8 takeaways.

1. The threat landscape now moves at machine speed.

AI has made exposure discovery instant. Your unknown exposures are now part of your attack surface, and threats are multiplying faster than most teams can triage. While most security organizations are responding by trying to move faster, speed without accuracy isn’t an advantage. Staying ahead means having intelligence that makes machine-speed defense more effective, not just fast.

As Truvé put it, intelligence has always been the way to stay proactive instead of reactive, and as "clock speed" increases across the industry, staying even a little ahead requires acting on intelligence faster than ever.

“External attack surface, security operations, vulnerability management, prioritizing — so many of these use cases and workflows take on a new level of urgency because of the speed component,” Gundert said.

2. "Defend the right things" is now a multi-bear problem.

The team agreed that the old security adage — you don't have to outrun the bear, just the person next to you — no longer holds.

AI removes that comfort almost entirely. Attackers only need one way in. Defenders have to cover just about everything. That asymmetry has always been the challenge and AI is making it structurally worse. It’s no longer one bear chasing the herd anymore — it's one bear chasing each member of the herd, since attackers can automate at scale even more efficiently.

3. Attacks are already becoming more clever, not just faster.

The panel discussed a real-world software supply chain compromise where attackers used compromised credentials to push a malicious package update, then had an LLM already present on infected developer machines search out AWS keys, SSH keys, and other credentials locally. The stolen data was encrypted and exfiltrated through a public GitHub repository — activity that never tripped EDR because it looked like ordinary LLM usage.

It was a preview of a much bigger wave of clever attacks that will likely quietly repurpose and weaponize the AI tools already installed on a target's machine.

4. Locking down devices isn't the only answer — context-aware access might be.

Locking down every endpoint isn't realistic, and it probably is not the answer. Situational permissions, such as access that flexes by location, time, and context are zero trust logic applied to the AI era.

5. Whether AI favors attackers or defenders depends on execution.

Everyone is talking about what AI can do. Fewer are asking who AI will ultimately benefit. Will the advantage belong to attackers or defenders?

It’s a question of how well organizations manage the trade-off between innovation and guardrails. Teams that articulate boundaries tend to build stronger solutions.

Truvé broadened the definition of "AI" beyond LLMs to include things like anomaly detection, and predicted an ongoing arms race. “At any given point in time, depending on who's more creative in using the new technology,” he said, “one side or the other will have the upper hand.”

6. Human-in-the-loop is a bridge, not a destination.

Expect humans to stay involved in approving high-stakes actions.

“Hopefully that just becomes very minimal decision points on all of the articulation that has been built around an incident,” Gundert said. “All the telemetry has been gathered, everything's been enriched. The agent wants to take an action. Maybe they want to isolate a machine on the network, maybe they want to revoke credentials, and it comes to you over Signal or Slack or whatever it is to hit Approve.” But he compared it to the first few rides in a self-driving car: Comfort builds with repeated exposure.

In five years, requiring a human in the loop for every decision may look as outdated as manual patch management does today — replaced by an expectation of an agent in the loop with lighter human oversight.

7. Intelligence will be critical for more than effective resource allocation.

With AI expected to surface a flood of newly discovered vulnerabilities, prioritization will become a major challenge. While work is being done to identify which vulnerabilities are likely to be weaponized and which are likely to target a given organization, the explosion of AI-generated "dark code" is predicted to expand the attack surface by as much as tenfold.

Intelligence should become the mechanism for deciding where limited security resources and token budgets get allocated first. This is why intelligence accuracy is paramount — organizations need to be able to prioritize the right things to defend.

8. Real-time data beats built-in model knowledge.

The team emphasized that relying on a model's internal knowledge risks working from information that's months old — precisely when speed matters most.

LLMs with search still only reach the open web and surface-level open-source intelligence. They don't have access to the technical detail or restricted spaces needed to understand what adversaries are actually doing.

And there are plenty of reasons an LLM won't deploy agents into the internet's bad neighborhoods — which is exactly why the underlying data feeding an intelligence program can matter as much as the model interpreting it.

Stay ahead of AI-enabled attacks.

AI is raising the stakes in terms of speed, prioritization, and trust in automation. The organizations that will weather what follows are building two things now: intelligence they can trust, and comfort with autonomous action.

They need to be able to:

• Prioritize with intelligence they can trust.
• Act pre-attack, at the first sign of threat.
• Defend at scale, autonomously.

Teams that invest now in high-quality, real-time intelligence — and start building comfort with agentic decision-making in lower-stakes workflows — will be better positioned when fully autonomous attacks eventually arrive.
Take Recorded Future’s interactive tour to see what defending at machine speed looks like in practice. Learn more and launch the interactive tour.

Watch the full conversation with Recorded Future leaders as they discuss how AI is reshaping threat intelligence, the evolving threat landscape, and what it means for defenders.

The United States (US) will almost certainly remain at heightened threat from physical threat activities conducted by homegrown and domestic violent extremists (HVEs and DVEs, respectively) during the next twelve months. Since the last installation of this report in July 2025, there has been a substantial increase in mass-casualty attacks and attack plots by Islamic State (IS) supporters, assassinations and attempted assassinations of US government officials and high-profile public figures by anti-government and anti-authority violent extremists (AGAAVEs), and multiple plots by anarchist violent extremists (AVEs) to cause substantial damage to facilities using destructive devices.

Coinciding with these trends, the Iran War will almost certainly have significant ramifications for the violent extremism threat landscape in the US over the next twelve months, even if the war has concluded. Based on Insikt Group’s observations to date, Iranian external operations are less likely to be a significant cause of violent extremist threat activity in the US than HVEs and DVEs motivated by the second-order sociopolitical and economic effects of the war. Iran almost certainly intends to guide or inspire HVEs to conduct physical threats in the US on its behalf. However, Iran-nexus physical threat activities in the US during the war have been largely aspirational, reliant on low-sophistication, financially motivated threat actors, and disrupted by law enforcement in the early planning stages of attacks. This likely suggests Iran lacks the capabilities necessary to guide large-scale HVE attacks in the US at present.

In contrast, the second-order effects of the Iran War will almost certainly add to the list of grievances and causes for mobilization popular among a wide swath of US violent extremists, likely increasing the risk of violent extremist physical threat activities during the next twelve months. Specifically, the war surfaced salient domestic and international political issues that have historically mobilized US violent extremists. These controversies include US military involvement in the Middle East; the relationships among the US, Israel, and other Middle Eastern countries; the state of the US economy; and the involvement of specific US private-sector entities in US foreign policy planning and military operations. In addition to motivating violent extremists, these issues will almost certainly influence US political discourse during the 2026 midterm elections in the US; the election cycle itself will very likely drive additional US violent extremist threats in late 2026 and early 2027.

Public- and private-sector organizations face differing levels of violent extremist threats due to their sector, industry, role, associations, and attack surfaces. While different categories of violent extremists employ various TTPs depending on their ideology and objectives, Insikt Group continues to assess that targeted attacks against facilities and high-profile personnel constitute the predominant violent extremist risks to most organizations. Overall, entities associated with US foreign policy decision-making, immigration enforcement, the Israeli government and military, or the provision of critical infrastructure services very likely face heightened risks from violent extremists. Threat intelligence — including features within the Recorded Future Intelligence Operations Platform — can support organizations’ efforts to identify, analyze, and mitigate threats posed by US-based violent extremists.

Key Findings

During the next twelve months:

• Iran-nexus physical threat actors will very likely attempt to target facilities and personnel in the US that they associate with the US or Israeli government and military, although the sophistication, impact, and frequency of these plots will likely be limited by Iran’s capability gaps.
• HVE IS supporters will be the most likely violent extremist threat actors to conduct mass-casualty attacks in the US, particularly during the period between October 2026 and January 2027.
• Instructional and ideological material produced by the neo-Nazi accelerationist movement will very likely continue to drive mass-casualty attack plots and sabotage against critical infrastructure, despite the fragmentation of neo-Nazi accelerationist organizations and online networks.
• AGAAVEs motivated by partisan animus will almost certainly pursue targeted physical attacks against high-profile public officials; secondary effects of the Iran War and the 2026 US midterm election cycle will very likely exacerbate these risks.
• AVEs will likely employ increasingly sophisticated and destructive means of conducting attacks targeting facilities and property, particularly targets they associate with US immigration enforcement policies

Background

Insikt Group’s forecast is predicated on its assessments of open-source information on the activities of HVEs and DVEs in the US, including Recorded Future’s extensive index of HVE and DVE communications on various online platforms. During the past twelve months, Insikt Group researchers curated several hundred HVE and DVE sources, adding them to an extensive index of preexisting sources of this type.

This report defines “homegrown violent extremist,” “domestic violent extremist,” and several categories of HVEs and DVEs based on the US Intelligence Community’s definitions and categorizations of threat actors. Periodically, this report uses definitions adopted by Insikt Group subject-matter experts for analytic utility, or in instances where the US government has not officially defined a particular phenomenon or movement. A full list of terms and definitions used in this report is available in Appendix A.

Homegrown Violent Extremists

The HVE threat to the US is very likely at its most severe level since the mid-2010s, during the period following the Islamic State’s rise to power and 2014 offensive to seize territory in Syria and Iraq. Jihadist Salafi HVEs, specifically IS supporters, very likely remain the most likely HVE threat actors to conduct mass-casualty attacks, despite a very likely resurgent threat from HVEs tied to Iran and its “axis of resistance” groups. While both jihadist Salafi HVEs and Iran-nexus HVEs almost certainly have the intent to conduct attacks in the US, we judge Iran-nexus HVEs have more limited capability to do so, due to a very likely smaller number of HVEs who support Iran or its “axis of resistance.”

Despite the June 17, 2026, memorandum of understanding (MoU) that established a ceasefire between the US and Iran, Iran’s military and intelligence agencies, proxy groups, and sympathizers in the US will very likely continue their efforts to guide physical threat activities on US soil, albeit with a greater focus on maintaining plausible deniability. The Iran War and the deaths of several notable members of Iran’s clerical and military leadership almost certainly removed most of the limits to Iran’s intent to target the US by proxy. However, Iran has not yet publicly demonstrated that it possesses sophisticated capacities to engage in physical threat activities on US soil. Observed plots during the Iran War involving Iran-nexus US-based physical threat actors have been largely aspirational, unsuccessful, and reliant on financially motivated (as opposed to ideologically motivated) threat actors.

While not direct participants in the conflict, IS, al-Qaeda, and other foreign terrorist organizations are likely to leverage second-order developments in the Iran War to further recruitment and radicalization of US HVEs. During the past three years, these groups have almost certainly positioned previous shifts in the Middle East’s geopolitical environment — notably Israeli military activity in Gaza and Lebanon following Hamas’s October 7, 2023, attack in Israel, the 2024 fall of the Bashar al-Assad regime in Syria, and diplomatic normalization of relationships between Israel and Muslim countries — to support influence narratives, generate propaganda, and reach a new generation of potential supporters. IS, which almost certainly remains at the forefront of the global jihadist Salafi movement, influenced a significant uptick in HVE threat activity in the US during the past twelve months, a dynamic that is very likely to continue in 2026 and early 2027.

HVEs of all varieties are very likely to target entities they associate with the US or Israeli government or military, private sector entities they associate with the US or Israeli government, Jewish communities, and large public gathering places. Most observed plots continue to use low-cost means, such as firearms, knives, incendiary devices, and vehicular attacks. Nevertheless, Insikt Group has monitored efforts by HVEs to share information on improvised explosive device (IED) manufacturing in online forums; there are additionally several known cases during the past year of HVEs successfully manufacturing IEDs for use in attacks. In addition, HVEs almost certainly will continue to experiment with novel tactics, techniques, and procedures (TTPs), including the use of 3D-printed weapons, unmanned aerial vehicle (UAV)-borne IEDs, and generative artificial intelligence (AI) for attack planning.

Iran and Axis of Resistance

The Iran War almost certainly increased Iran-nexus physical threat actors’ motivation to conduct attacks, sabotage, arson, and defacement in the US, as a form of Iran’s asymmetric retaliation against the US. The cessation of direct hostilities between Iran and the US is very unlikely to deter Iran-nexus threat actors from carrying out physical threat activities, although, post-MoU, these threat actors are very likely to pursue more covert and less destructive TTPs. In rank order, the most likely targets of these activities are:

• Targets perceived to be associated with Israeli or Jewish communities
• High-profile US, Israeli, and Western foreign policy and military officials
• Iranian dissidents residing abroad
• Private-sector organizations affiliated with the US or Israeli military, particularly defense contractors, insurance companies, banks and financial institutions, and critical infrastructure service providers

There are almost certainly few remaining strategic or ideological barriers to Iran’s aspirations to guide attacks on US soil, even after the June 2026 ceasefire agreement with the US. While the June 2026 MoU stipulates that Iran must “refrain from interfering in [the US’s] affairs,” it is unclear whether Iran would interpret non-interference to include Iran-nexus physical threat activities. Additionally, Iranian security agencies may choose not to abide by the clause, especially given how the conflict empowered hardliner elements within Iran’s security apparatus and granted them significant autonomy in operational decision-making. Tehran also very likely would not interpret the clause as applying to Iran’s “axis of resistance” groups. Regardless, Iran has attempted to solicit HVEs to conduct attacks in the US for decades, even during periods without direct military confrontation with the US. During the Iran War, the US killed several senior religious and military figures within Iran (including Ayatollah Ali Khamenei); the deaths of senior Iranian leaders have historically been harbingers of Iran-nexus physical threat activity in the US. Since the beginning of the Iran War, senior Shi’a Muslim clerics in Iran have issued rulings encouraging Muslims around the world to avenge Khamenei’s death by targeting the US and Israel. Iran has also leveraged online influence operations networks to recruit individuals to carry out attacks in the US, and has very likely inspired attack plots in the US. Iran will very likely attempt to ensure any post-MoU external operations in the US are deniable and avoid mass-casualty attacks or assassinations of high-profile public figures — to avoid provoking the US — but there almost certainly remain no significant ideological or strategic deterrents to Iranian external operations as a whole.

Regardless of its intent, however, Iran likely lacks access to a significant number of US-based, ideologically sympathetic HVEs, limiting its external operations capabilities in the US. During the last decade, Iranian operators predominantly tied to the Islamic Revolutionary Guard Corps (IRGC) attempted to pay members of transnational criminal organizations (TCOs), petty criminals, and other financially motivated threat actors to conduct attacks in the US. Insikt Group’s observations of Iran-nexus physical threat activity post-February 2026 indicate this threat model did not change due to the Iran War. For instance, in April 2026, a commander of the IRGC’s Iraq-based proxy Kataib Hezbollah (KH) and its external operations-focused persona Islamic Movement of the Companions of the Right (IMCR, also known as Ashab al-Yamin and HAYI) allegedly attempted to recruit a Federal Bureau of Investigation (FBI) undercover officer — whom he believed to be a US-based Mexican TCO member — to conduct attacks on several synagogues in the US, offering the undercover officer $10,000 in cryptocurrency.

Figure 1: April 20, 2026, IMCR statement threatening US President Donald Trump and his family. (Source: Recorded Future)

How do you prove a negative in cybersecurity? How do you prove that you weren’t attacked, or that there is no intruder in your network? These are questions that security teams have been forced to ask for a while, but there is a new question that is becoming increasingly common: How do you prove that files weren’t stolen from your network? Or, even more of a challenge, how do you prove that files weren’t stolen from your partners, vendors, or their partners or vendors?

This is a surprisingly challenging question to answer. Finding the answer is also more difficult because data governance has not been the traditional purview of security teams. Data governance has long been thought of as a compliance problem, unfortunately that is no longer the case. Security teams are now, whether they want to be or not, need to consider data governance. This means they have to be able to confidently say whether leaked data is real or not.

How do you do that?

History of Ransomware

What we call ransomware has evolved over the years. Ransomware has gone from largely focused on encryption to a combination of encryption and data theft to today’s reality where data theft alone is the most common version of a “ransomware” attack.

Threat actors have figured out that managing encryption keys is challenging, stealing data and holding it hostage is significantly easier. They’ve also figured out that stealing the right data can be just as profitable as encryption and, as we’ve seen from ransomware trends, switching to data theft only allows groups to accelerate the number of attacks. Compare the number of victims from 2024 to 2025 in the Recorded Future® Ransomware dashboard with a noticeable rise in ransomware trends.

13,000.

That’s the number of ransomware victims Recorded Future has observed over the past two years.

Watching the near-real-time ransomware attacks on businesses, non-profits, and government agencies has left me, like many security professionals and board directors, pondering how and why cyber defense keeps losing this particular fight. Adversaries like Interlock and RansomHub have continued their successful march to riches over the past 18 months. The multi-billion-ruble question is, “How?”

BloodHound and the defensive graph concept debuted over a decade ago and still maintain a vibrant open-source community. Continuous Threat Exposure Management (CTEM) (and attack path management) is an established cyber vendor category, yet ransomware crews are demonstrably eating many organizations’ lunch.

Let’s explore the problems (which are relatively easy to enumerate) and a solution (harder): modeling defense as the graph attackers actually traverse, at the speed they traverse it, which, of course, involves intelligence.

The Barometer

Ransomware is a solid barometer of operational defensive success, specifically because, unlike espionage, it’s noisy, financially motivated, and opportunistic. Certainly, ransomware also benefits from an optimal ecosystem, including payment economics, cyber insurance playbooks, and jurisdictional safe havens, which help incentivize ransomware gangs to find the cheapest attack paths. Relatively inexperienced actors can pick up commodity tools and reach the crown jewels. That highly repeated Ransomware-as-a-Service (RaaS) dynamic is a verdict on the availability of attack paths, regardless of payment incentives.

Insikt Group identified four new TAG-195 ("Golden Chickens", “Venom Spider”) malware families through ongoing tracking of the TAG-195 MaaS ecosystem. We named two of the families "TinyEgg" and “ChonkyChicken"; the third is a modularized variant of ChonkyChicken. The fourth family, which includes a modified browser credential theft helper, we named “ChromEggscalator". TAG-195 is a financially motivated malware-as-a-service (MaaS) developer whose tooling Insikt Group has previously linked to TAG-127 as an operator and customer. (Insikt Group has directly observed TAG-127 deploying TinyEgg via “ClickFix”-style campaigns that use fake security verification pages to trick victims into manually executing malicious commands that download and install malware payloads via a legitimate Windows system utility.)

The four new families indicate an architectural transition and evolution in the TAG-195 MaaS ecosystem. TinyEgg is a lightweight initial-access backdoor providing host profiling, interactive shell access, and persistence management. ChonkyChicken substantially expands that capability with browser credential theft, browser session automation, credential-backed remote execution, network reconnaissance, and sustained surveillance. The modularized ChonkyChicken extends this design by introducing a controller-and-plugin architecture in which a base controller implant requests and loads discrete capability modules from attacker-controlled infrastructure on demand rather than embedding all functionality in the implant itself. TAG-195 also modified a publicly available Chrome encryption-bypass tool into a custom helper within the malware family that Insikt Group named ChromEggscalator. All four families share a common set of architectural traits: consistent command-and-control mechanisms, a shared persistence approach, string obfuscation, and execution via the same delivery model.

Insikt Group assesses that TAG-195’s transition to a modular architecture almost certainly reduces the base implant's static detection exposure, and likely also reflects commercial incentives inherent to the MaaS model, including the ability to provision capabilities selectively to operators, limit exposure if a customer is compromised, and serve a broader range of operational requirements. Defenders should prioritize detection of ClickFix-style clipboard execution chains, misuse of legitimate system utilities to load payloads from user-writable directories, suspicious startup persistence mechanisms, browser processes launched with remote debugging enabled, and unusual outbound communications to attacker-controlled infrastructure.

Key Findings

• Insikt Group identified four new TAG-195 malware families through its continued tracking of the TAG-195 MaaS ecosystem: TinyEgg, ChonkyChicken, a modularized variant of ChonkyChicken, and ChromEggscalator. Their identification indicates sustained active development and a deliberate architectural transition toward modular, operator-driven tooling.
• The modularized ChonkyChicken variant uses a controller-and-plugin architecture in which a base controller implant requests and loads at least fourteen capability modules on demand. Insikt Group assesses that this design almost certainly reduces the base implant's static detection footprint while enabling operators to deploy only what each intrusion requires.
• All four malware families share four recurring architectural traits that indicate their origin within the same TAG-195 development ecosystem: filename execution gating, Run key persistence under a consistent value name, string obfuscation, and execution via a legitimate Windows binary.

Background

TAG-195, also known as “Golden Chickens” or "Venom Spider", is a financially motivated MaaS developer with a long-standing history of providing credential theft and remote access tooling to criminal operators. Insikt Group assesses TAG-195 as a MaaS provider based on the availability of its malware to multiple distinct threat actors and its sustained operation across successive generations of tooling. Public reporting by eSentire has previously linked TAG-195 tooling to FIN6, Cobalt Group, and Evilnum, three financially motivated criminal groups, suggesting the ecosystem serves a select customer base; however, details on sales models and access conditions remain unknown. Additionally, Insikt Group tracks TAG-127 as a threat group that uses the TAG-195 MaaS, with ClickFix or VenomLNK as delivery methods.

Figure 1: TAG-195 threat group associations (Source: Recorded Future)

• Modern threat actors have shifted from brute-forcing firewalls to compromising digital identities via stolen session cookies and credential stuffing to bypass MFA entirely
• Adversaries increasingly target unpatched edge infrastructure like VPNs for zero-day access while exploiting open-source repositories to launch upstream supply chain attacks
• Traditional internal security telemetry may miss critical pre-attack signals, making real-time, outside-in threat intelligence essential to neutralizing modern vectors before a breach occurs

For today’s Chief Information Security Officers (CISOs) and security team leaders, defending your business can feel like trying to hold back the ocean. As organizations rapidly scale cloud-native infrastructure, integrate sprawling third-party ecosystems, and adopt enterprise AI workflows, most organizations' digital footprints have exploded.

But a massive digital footprint isn’t the core problem. The problem is that adversaries are changing how they navigate it.

Advanced persistent threats (APTs) and sophisticated cybercriminal syndicates are no longer relying on blunt-force intrusions. Instead, they are tracking organizational vulnerabilities from the outside in, using targeted methods to slip past defenses unnoticed. To stay ahead, security leaders must look past traditional, inward-facing security telemetry and think more like the adversary. That begins with a precise, real-time understanding of modern attack vectors.

What is an Attack Vector?

In cybersecurity, an attack vector is the specific path, route, or method an adversary uses to gain unauthorized access to a network, system, or endpoint to deliver a malicious payload or extract data. If an exploit is the lockpick, the attack vector is the hallway the intruder walked down to reach the door.

Historically, attack vectors were relatively straightforward. A decade ago, an enterprise might primarily worry about phishing emails containing malicious executable attachments or unpatched, internet-facing servers.

In 2026, attack vectors have evolved from isolated incidents into complex, multi-stage journeys. Modern adversaries rarely rely on a single open door. Instead, they link multiple vectors together to achieve their objectives.

For example, a modern threat actor might initiate an intrusion using an automated multi-factor authentication (MFA) fatigue campaign to compromise a low-level employee identity, pivot through an exposed, undocumented API, and ultimately execute a ransomware payload via a trusted third-party software update.

Attack Vector vs. Attack Surface: What’s the Difference?

While they are frequently used interchangeably in security discussions, conflating your attack vectors with your attack surface can create fundamental gaps in your defensive strategy.

• An Attack Surface is the sum total of all potential vulnerabilities, exposure points, and digital assets across an organization’s entire footprint that an unauthorized user could try to enter or extract data from—including public cloud buckets, employee credentials, IoT devices, code repositories, and vendor networks.
• An Attack Vector is the specific vehicle, mechanism, or strategy used to exploit a precise point on that surface. It is the active "weapon" or method of transit chosen by the hacker.

Think of your organization as a fortified castle. The attack surface is the entirety of the castle's physical structure—every wall, window, gate, and underground passage. The attack vector is the specific ladder, battering ram, or sleeping guard the invading army uses to breach a specific point on that structure.

Defending the attack surface requires comprehensive visibility into what you own. Neutralizing an attack vector requires real-time intelligence on how adversaries are actively weaponizing their toolkits.

What Threat Actors Are Actively Targeting in 2026

Adversary tactics are driven by efficiency and return on investment (ROI). In 2026, threat actors largely abandoned brute-force attacks on hardened corporate firewalls. Instead, they target systemic structural weaknesses across three primary dimensions:

Identity as the New Perimeter

Identity has emerged as the definitive battleground for enterprise security. Rather than breaking in, modern threat actors simply log in. Defenses have been circumvented by the massive industrialization of the cybercrime underground, where initial access brokers (IABs) and infostealer malware supply millions of stolen session cookies and valid credentials daily.

Adversaries can use credential stuffing to bypass traditional authentication, target cloud identity providers (IdPs) directly, and leverage session hijacking to step over MFA entirely—rendering standard boundary defenses obsolete.

Edge Infrastructure and Software Supply Chain Vulnerabilities

The perimeter has moved to the edge, and adversaries have followed. Over the past few years, we have seen a significant surge in threat actors targeting unpatched edge devices—such as VPN gateways, firewalls, and edge routers—to secure zero-day footholds directly into corporate networks. Simultaneously, the software supply chain has become a highly lucrative upstream vector. By poisoning open-source repositories or compromising trusted third-party dependencies, adversaries can affect thousands of downstream organizations in a single, silent stroke.

AI-Driven Exploitation and Prompt-Based Manipulation

Generative AI has fundamentally altered the velocity and scale of modern attack vectors. Threat actors now leverage automated LLM orchestrations to generate personalized social engineering campaigns and deepfake audio/video that can easily deceive even well-trained employees. Even as enterprises rush to integrate AI into internal workflows, new vectors like prompt injection and data poisoning have transitioned from theoretical concepts to active threat vectors, allowing adversaries to manipulate LLM outputs and extract proprietary enterprise data.

Why Traditional Security Frameworks Cannot Stop Modern Attack Vectors

Most enterprise security architectures were built for a static world that no longer exists. When confronted with the dynamic vectors of 2026, traditional frameworks break down in two distinct ways:

Static Vulnerability Management

Many security operations centers (SOCs) remain tied to traditional vulnerability management models that prioritize patching based strictly on CVSS scores. This creates a dangerous blindspot. Advanced persistent threats intentionally chain together multiple "low-severity" or "medium-severity" vulnerabilities that, when combined, can grant full administrative access.

Manual asset discovery tools also struggle to keep pace with ephemeral cloud environments, creating visibility gaps that turn unmapped assets into instant attack vectors.

The Outside-In Blindspot

Internal security teams are naturally focused on internal telemetry—pouring over logs inside their SIEM, EDR, and NDR tools. However, this creates a reactive stance. By the time an adversary triggers an EDR alert, the attack vector has already been successfully executed. Internal telemetry is often blind to pre-monetization signals: the registration of typosquatted domains targeting your brand, the sale of corporate credentials on dark web marketplaces, or the collaborative planning occurring in closed adversary forums.

Neutralizing Modern Attack Vectors with Recorded Future

To defeat adversaries who operate at the speed of automation, organizations must shift from a reactive posture to a proactive, intelligence-led defense. Recorded Future provides the external visibility and real-time intelligence required to map, prioritize, and dismantle modern attack vectors before they breach your perimeter.

Cyber Operations: Shifting from Reactive Response to Machine-Speed Defenses

Faced with overwhelming alert fatigue, SOC teams cannot afford to chase every theoretical vulnerability. Recorded Future Cyber Operations acts as the antidote to operational noise. Powered by the Intelligence Graph®, which continuously sifts through millions of global data points, it automatically prioritizes vulnerabilities based on live, real-world exploitation data rather than static CVSS math.

By enriching your existing internal tools (SIEM, EDR, SOAR) via Collective Insights®, Recorded Future injects real-time adversary Tactics, Techniques, and Procedures (TTPs) directly into your workflow, enabling defenders to triage alerts and block active attack vectors at speed.

Digital Risk Protection: Securing the External Attack Surface

You cannot defend against an attack vector you cannot see. Recorded Future Digital Risk Protection provides an outside-in view of your organization, mapping your external attack surface, mirroring how an adversary scans it.

By monitoring open, deep, and dark web sources, it identifies compromised corporate credentials, active typosquatted phishing domains, and source code exposures on public repositories. This visibility allows security teams to take down malicious infrastructure and revoke compromised access before threat actors can convert them into active entry points.

Third-Party Risk: Closing the Vendor Supply Chain Gap

Relying on annual, static security questionnaires to assess vendor risk is the equivalent of checking the weather once a year and assuming it will never rain. Third-Party Risk replaces outdated point-in-time assessments with continuous, automated risk monitoring.

Providing real-time Risk Scores (ranging from 0-99) and mapping complex fourth-party ecosystem dependencies, it alerts your team the moment a vendor within your supply chain shows signs of compromise. This enables you to isolate vulnerable connections long before an upstream vendor breach turns into your downstream crisis.

Payment Fraud: Disrupting Fraud Lifecycles

For financial institutions and e-commerce enterprises, the attack vector of choice often targets transaction infrastructure. Recorded Future Payment Fraud can disrupt the fraud lifecycle by monitoring pre-monetization signals.

By identifying Magecart e-skimmers on digital storefronts, monitoring underground carding forums, and spotting tester merchant activities in real time, Recorded Future allows organizations to fraud-check and block compromised payment cards before fraudulent transactions hit the bottom line.

Proactive Mapping Leads to Resilient Defense

In 2026, understanding your attack vectors can no longer be treated as a check-the-box compliance exercise or a periodic audit. Adversaries are highly dynamic, highly automated, and constantly scouting for the path of least resistance across your digital footprint.

True organizational resilience requires continuous, automated external intelligence. By seeing your enterprise exactly the way the adversary sees it, you can move from a state of constant reaction to one of strategic deterrence.

Don't wait for an alert to tell you your perimeter has been breached. Book a demo with Recorded Future today to gain real-time visibility into your external attack surface and neutralize modern threat vectors before they unfold.

To survive in this environment, modern cyber defense teams must anchor their strategy to a single, non-negotiable rule: Assume you are already breached. Waiting for an automated alert to trigger is a losing strategy. Proactive cyber threat hunting shifts the power dynamic from reactive firefighting to active, aggressive detection. Human analysts alone cannot process the volume and velocity of data required to detect sophisticated adversaries at enterprise scale. To truly master modern threat hunting, security teams should consider enriching internal telemetry with real-time, external threat intelligence.

Understanding threat hunting

At its core, threat hunting is the practice of proactively and iteratively searching networks, endpoints, and cloud environments to detect and isolate advanced threats that evade existing security solutions. It is a human-led, hypothesis-driven discipline—not a purely automated feature of a software suite.

Here is how it differs from other standard security functions:

• Threat Hunting vs. Incident Response
  Incident response is fundamentally reactive; it is the act of extinguishing an active, visible fire after an alert has triggered. Threat hunting is proactive, searching the architecture for hidden threats before they erupt into a catastrophic breach.
• Threat Hunting vs. Penetration Testing
  Penetration testing evaluates perimeter defenses from the outside in, evaluating whether a simulated adversary can breach the network. Threat hunting operates under the explicit assumption that the attacker is already firmly rooted inside, hunting them down from within.
• Threat Hunting vs. Vulnerability Assessments
  Vulnerability management focuses on patching open windows and updating code to prevent future exploitation. Threat hunting assumes an attacker has already gained access and focuses on detecting their lateral movement before damage is done.

What teams need to begin threat hunting

An effective threat hunt cannot begin in a vacuum. Before analysts can root out sophisticated threat actors, organizations must establish a baseline foundation across three core pillars: visibility, integration, and external context.

1. Visibility

Threat hunting requires deep, centralized internal telemetry logs, including:

• Endpoint Event Logs (EDR Data): Process execution trees, registry modifications, and local network connections.
• Network Traffic Analysis (NTA): NetFlow data, DNS queries, and TLS handshake anomalies.
• Identity & Access Management (IAM) Logs: Cross-zone authentication spikes, anomalous MFA prompts, and privilege escalations.

2. Tool integration

Relying on isolated data silos paralyzes analysts. Security teams are recommended to leverage unified SIEM and SOAR integrations to aggregate disparate data sets, normalize log schemas, and eliminate the white noise of benign network activity.

3. External intelligence

Analyzing internal logs without external context is like looking at footprints in the mud without knowing what animal made them. Deep web, dark web, and technical intelligence should be required, providing the exact behavioral profiles, infrastructure layouts, and campaign contexts needed to guide the hunt.

The 3 Core threat hunting methodologies

1. Hypothesis-Driven Hunting

This methodology relies on a baseline understanding of an organization's unique threat profile. Rather than chasing random anomalies, hunters form educated, structured theories based on environmental risk.

For example: "If an advanced persistent threat (APT) targets our specific financial services vertical using a known cloud-storage exploit, do those specific forensic artifacts exist in our environment right now?" Analysts then construct targeted queries to validate or disprove the theory.

2. Intelligence-driven hunting (IOC & TTP mapping)

Tactical and operational intelligence can serve as the blueprint for tracking down precise adversary patterns. By mapping observed threat intelligence—such as malicious IP addresses, command-and-control (C2) domains, newly announced CVEs, and adversary Tactics, Techniques, and Procedures (TTPs)—directly to the MITRE ATT&CK® framework, hunters can systematically search internal logs for identical behavioral signatures.

3. Advanced analytics & AI hunting

This approach uses behavioral profiling and data stacking to isolate structural outliers from massive datasets. By evaluating thousands of similar data points, machine learning models highlight anomalous user or machine actions—such as a standard HR user account suddenly executing administrative command-line scripts or initiating mass data transfers at 3:00 AM.

The Lifecycle of a proactive cyber threat hunt

A successful threat hunt follows a structured, iterative lifecycle. By injecting external threat intelligence into every phase, analysts can transform an ad-hoc search into an accelerated, scalable defensive program.

Step 1: Let intelligence drive your hunt

The hunt begins when an analyst defines a focused area of inquiry based on a structured hypothesis. This initial trigger is driven by real-time threat intelligence regarding an active campaign, an emerging zero-day vulnerability, or a newly discovered infrastructure cluster belonging to a relevant threat actor family.

Step 2: Architect your hunt at scale

Once the hypothesis is set, hunters deploy advanced threat hunting tools to translate technical indicators into sweeping enterprise queries. Analysts architect data-gathering parameters across disparate EDR databases, SIEM platforms, and network traffic monitors to ensure better visibility across the entire enterprise footprint without manual bottlenecking.

Step 3: Activate autonomous threat hunting

Rather than executing one-off, static searches that instantly age out, teams deploy continuous automated playbooks. By integrating real-time intelligence directly into detection engines, cyber threat hunting teams are able to shift from an ad-hoc manual task to a 24/7 autonomous monitoring process that tracks evolving adversary behavior in real time.

Step 4: Review correlated findings

When anomalous activity matches the hunt parameters, analysts evaluate the high-fidelity telemetry alongside external intelligence inputs. If malicious activity is verified, the hunt instantly pivots to incident response for isolation; if the anomaly is benign, the findings are fed back into the security ecosystem to update rules and eliminate future noise.

Step 5: See the impact with AI reporting

The final phase translates complex forensic data into strategic business metrics. By leveraging automated, intelligent reporting, security leaders instantly visualize the hunt’s operational impact—documenting exactly which assets were protected, how dwell time was mitigated, and how defensive postures were permanently hardened against future attack vectors.

Where modern threat hunting can fall short

Executing a continuous, high-yield threat hunting program presents severe operational friction points for modern CISOs and SOC managers:

• The cybersecurity skills shortage: Seasoned threat hunters require a rare blend of data science, digital forensics, and adversary mindset analysis. These professionals are incredibly scarce, highly sought after, and financially burdensome to recruit and retain.
• Alert fatigue and false positives: Analysts spend hours chasing benign data anomalies because legacy threat hunting tools lack external context. Without real-time enrichment, an unusual out-of-hours connection looks identical to a critical C2 beaconing event.
• The time-to-exploit collapse: The window between a vulnerability being announced on the clear web and actively weaponized on the dark web has shrunk to mere hours. Static, ad-hoc hunting schedules often cannot keep pace with this compressed timeline, leaving networks exposed between manual hunts.

Mastering the hunt with Recorded Future

Recorded Future reduces these operational bottlenecks, transforming threat hunting from a resource-draining manual grind into an accelerated, intelligence-led defense mechanism.

The Intelligence Graph®

Recorded Future’s Intelligence Graph® continuously monitors open sources, technical infrastructure, and illicit dark web forums. By analyzing billions of entities in real time, it delivers a live map of global threat actors, emerging malware families, and weaponized vulnerabilities. This gives threat hunters visibility into external shifts before they are able to impact internal networks.

Reducing manual triage

Instead of forcing tier-3 analysts to waste critical hours pivoting across dozens of open-source intelligence (OSINT) browser tabs, Recorded Future delivers instantly actionable context. Internal alerts within your SIEM and EDR are automatically enriched and tagged with real-time threat-actor details, Risk Scores, and mapped TTPs, allowing hunters to identify high-risk anomalies instantly.

Insikt Group® insights

Security teams no longer need to spend days writing complex detection logic from scratch. Recorded Future’s Insikt Group®—an elite team of veteran threat researchers—delivers pre-written, expert-vetted YARA, Snort, and Sigma rules directly into your existing SIEM, SOAR, and EDR environments. This can turn global threat discoveries into immediate, internal defensive barriers.

Cyber Operations: unified intelligence for modern hunters

To truly scale a threat hunting program, security teams need to bridge the gap between external intelligence and internal workflows. Recorded Future Cyber Operations centralizes this process by mapping real-time adversary infrastructure, campaigns, and malware behaviors directly to the MITRE ATT&CK® framework. By delivering instantly deployable hunting packages alongside curated operational context, Cyber Operations can reduce the time it takes for analysts to shift from an external intelligence trigger to an active, internal environment scan.

Autonomous Threat Operations

To solve the persistent challenge of understaffed security teams, Recorded Future delivers Autonomous Threat Operations. By executing continuous hunting, detection, and response workflows autonomously, the Platform constantly scours your environment for complex threats. This elevates your defensive posture 24/7, freeing human analysts to focus on high-level strategic risk management.

The future of threat hunting

Modern threat hunting is no longer about working harder or writing longer queries; it is about hunting smarter. As adversaries exploit automation and compressed execution timelines, security teams should not rely on internal telemetry alone to defend the enterprise. Combining sharp human analyst logic with the most comprehensive threat intelligence platform available is how security teams can transition from reactive defense to proactive, intelligence-led threat hunting at enterprise scale.

Don't let advanced adversaries dictate the timeline of your security operations. Book a demo today to supercharge your threat hunting program and secure your environment from the inside out.

Threat hunting FAQs

What is cyber threat hunting in simple terms?

Cyber threat hunting is the proactive, human-led practice of systematically searching through an organization's networks, endpoints, and data repositories to detect malicious actors or hidden threats that have already bypassed automated perimeter defenses.

What are the common methodologies or triggers for a threat hunt?

Threat hunts generally rely on three types of investigations: hypothesis-driven (triggered by new adversary tactics, techniques, and procedures or TTPs), intelligence-driven (triggered by specific indicators of compromise or IOCs), and analytics-driven (triggered by machine learning detecting structural anomalies in network traffic behavior).

How does threat hunting differ from digital forensics and incident response (DFIR)?

Incident response and digital forensics are inherently reactive—they kick off after a security control fires an alert or a breach is publicly known to contain damage. Threat hunting is aggressively proactive; it assumes a breach has already occurred silently and searches for active adversaries before they trigger an alert.

How does Recorded Future accelerate the threat hunting process?

Threat hunting traditionally requires manual data gathering across disjointed open-source platforms. Recorded Future Cyber Operations can collapse this timeline by automatically mapping external adversary infrastructure, campaigns, and malware behaviors directly to the MITRE ATT&CK framework. It delivers instantly deployable hunting packages alongside pre-written YARA, Snort, and Sigma rules to enable a shift in a hunter’s workflow from manual intelligence gathering to immediate data interrogation.

• Advanced Persistent Threats (APTs) are sophisticated, long-term cyber campaigns conducted by well-funded human adversaries (often nation-states) who target specific organizations for espionage, data theft, or critical infrastructure disruption.
• Traditional security tools often fail because APT groups bypass signature-based defenses by using customized malware and Living-off-the-Land (LotL) tactics that mimic legitimate user activity inside the network.
• Effective advanced persistent threat detection requires minimizing breakout time, the window between initial access and lateral movement, by identifying threats before they establish deep persistence.
• To defeat modern APTs, organizations must move from reactive internal monitoring to proactive threat intelligence, tracking adversary infrastructure on the open, deep, and dark web before an attack is launched.

Modern organizations face highly resourceful, patient, and deeply calculated adversaries. This shift has ushered in an era of coordinated operations where elite threat actors don't just compromise a system and leave, but may spend weeks or months quietly surveying networks, mapping architecture, and identifying high-value targets.

These operations are the hallmark of an advanced persistent threat (APT). Traditional cybersecurity frameworks have long relied on perimeter defenses designed to catch malicious activity at the gates. However, once an APT group breaches a network, they often intentionally manipulate native administrative tools and harvest legitimate credentials to blend into daily business traffic.

To better confront an adversary that behaves like an insider, organizations must shift their perspective outward, leveraging real-time, external threat intelligence to identify and intercept cyber threats before they can establish a permanent foothold.

What is an Advanced Persistent Threat (APT)?

An APT is a sophisticated, prolonged cyber campaign executed by a highly organized group with specific, long-term objectives. Breaking down the acronym highlights the unique nature of these threats:

• Advanced: APT actors do not rely on off-the-shelf exploits. They frequently utilize customized malware, discover and weaponize zero-day vulnerabilities, and practice meticulous operational security (OpSec) to deliberately evade modern security controls.
• Persistent: Unlike cybercriminals who encrypt a server and immediately demand a ransom, APTs utilize a "low-and-slow" methodology. They prioritize stealth over speed, regularly remaining inside an environment for months to achieve strategic goals such as espionage, intellectual property theft, or the long-term disruption of critical infrastructure.
• Threat: Behind every APT is a well-funded organizational structure. These are not lone hackers; they are highly structured syndicates and state-sponsored units—such as the Lazarus Group or APT41—backed by massive financial and geopolitical resources.

The multi-stage APT attack lifecycle

Generally, APT groups do not operate at random. They follow a rigorous, multi-stage lifecycle. For defenders, understanding this timeline is critical to shrinking “breakout time"—the vital window between the initial compromise and the moment the attacker begins moving through the network.

1. Reconnaissance and planning

Before a single line of malicious code is deployed, attackers gather open-source intelligence (OSINT), scan exposed internet-facing infrastructure, and map out the target’s digital footprint to find weak points.

2. Initial infiltration

Attackers typically gain entry via hyper-targeted spear-phishing or social engineering campaigns, credential stuffing, or complex supply chain compromises, often bypassing standard authentication checks.

3. Establishing footholds

Once inside, actors deploy stealthy backdoors and obfuscated rootkits. This ensures that even if security teams discover and close the primary entry vector, the attackers maintain alternative entry routes.

4. Lateral movement and escalation

Adversaries navigate from system to system, harvesting administrative credentials and mapping Active Directory trust boundaries to compromise the enterprise network.

5. Data exfiltration or disruption

The group gathers, stages, and quietly extracts sensitive data using encrypted command-and-control (C2) channels. In some cases, they may deploy ransomware or execute a DDoS attack as a distraction to cover their tracks.

Why traditional advanced persistent threat detection isn’t enough

For Cyber Threat Intelligence (CTI) teams, threat hunters, and SOC managers, keeping pace with APTs using legacy tools is an uphill battle. Traditional detection tools and processes consistently fail against advanced actors for several reasons:

• Signature-Based Defenses: Legacy firewalls and traditional antivirus rely on known file hashes. Because APT groups write custom code and heavily leverage Living-off-the-Land (LotL) tactics using native administrative tools, they can leave no traditional signatures behind.
• Dwell Time: Internal log correlation through SIEM and EDR platforms is inherently reactive. If your team is only looking at alerts generated inside your perimeter, the attacker may have already achieved a foothold and begun their mission.
• Alert Fatigue and Data Silos: SOC teams are often drowning in a sea of disconnected internal alerts. Without external context, it is nearly impossible to distinguish a routine network anomaly from an APT group spinning up a new unclassified C2 server.
• Fragmented Vendor Taxonomies: Tracking adversaries across the industry is notoriously confusing. One threat group might be designated by a weather pattern by one vendor, an animal by another, or a random number by a third, complicating cross-team collaboration and intelligence sharing.

Shifting from reactive defense to real-time intelligence

To better counter advanced persistent threats, organizations must meet bad actors earlier in the attack lifecycle. This means disrupting the adversary during their reconnaissance and infrastructure-staging phases, long before they ever execute an exploit on an internal endpoint.
Real-time threat intelligence in the context of APTs means continuously harvesting, analyzing, and structuring data from across the open, deep, and dark web to monitor attackers as they build their technical infrastructure.

By tracking newly registered domains, malicious IP allocations, and discussions on illicit forums, defenders can identify a threat actor's setup phase. Mapping these observations to the MITRE ATT&CK® framework allows security teams to decode the specific Tactics, Techniques, and Procedures (TTPs) of an adversary, enabling them to anticipate and block the attacker's next move.

Mastering APT detection with Recorded Future

Recorded Future equips threat hunters and CTI analysts with the visibility needed to track advanced persistent threats across every stage of the attack lifecycle. By centralizing automated collection and elite human analysis, Recorded Future converts massive volumes of public and dark web data into actionable, proactive defense.

The Intelligence Graph®

The Recorded Future Intelligence Graph® automatically maps, links, and updates relationships between billions of entities—including IPs, domains, malware strains, and threat groups—across massive global datasets in real time, giving defenders an unparalleled view of adversary infrastructure.

Third-Party Risk

Sophisticated threat actors frequently target weak links in an enterprise ecosystem. With Third-Party Risk, organizations gain real-time visibility into the security postures of their vendors, contractors, and partners, cutting off supply-chain entry vectors.

Insikt Group®

Recorded Future’s elite network of threat researchers, the Insikt Group, acts as an extension of your security team, providing the latest geopolitical intelligence. They deliver pre-vetted, highly contextual information and actionable hunting rules (including YARA, Sigma, and Snort) directly into the Platform, allowing security teams to rapidly deploy defenses against emerging state-sponsored campaigns.

Recorded Future AI

Generative AI capabilities reduce Mean Time to Respond (MTTR). Analysts can use natural language to query complex APT behaviors, instantly surface connection points, and generate comprehensive, shareable intelligence briefs in seconds, streamlining leadership communications during critical events.

Staying one step ahead of cyber threats

Advanced persistent threats win when they remain hidden in the noise of a network. True detection requires looking beyond internal firewalls and endpoints, demanding visibility into the external environments where adversaries plan, build, and launch their operations.

In the face of highly organized, nation-state-backed syndicates, speed and visibility are the ultimate metrics of success. By shifting from a reactive internal posture to a proactive, real-time intelligence strategy, organizations can illuminate adversary infrastructure, disrupt the attack lifecycle, and secure their digital perimeter against even the most patient and well-resourced threat actors.

Want to see how real-time intelligence can transform your threat hunting capabilities? Book a demo with Recorded Future today.

FAQs

What is the primary objective of an advanced persistent threat (APT) group?

Unlike typical cybercriminals who seek immediate financial payouts through rapid encryption or ransomware, the primary objective of an APT group is usually long-term cyber espionage. Backed by nation-states or heavily funded syndicates, these actors aim to establish an undetected, prolonged presence within a target network to quietly steal intellectual property, harvest state secrets, or maintain access to critical infrastructure for future geopolitical leverage.

Why is advanced persistent threat detection so difficult for traditional security tools?

Traditional security tools rely heavily on static signatures—meaning they look for known, previously identified file hashes or malicious code patterns. APT actors easily bypass these defenses by writing customized malware, exploiting zero-day vulnerabilities, and using "Living-off-the-Land" (LotL) tactics that abuse legitimate system administration tools already built into your network. Because their activity mimics normal administrative tasks, they go unnoticed by internal firewalls.

What is "breakout time," and why does it matter in tracking APTs?

Breakout time is the critical window between an adversary's initial compromise of a single machine and their ability to move laterally to other systems on the network. For elite APT groups, this window can be incredibly tight. Tracking threat actor infrastructure in real time allows security teams to recognize the initial entry vector immediately and stop the actor before they can escalate privileges or move beyond the original target endpoint.

How does generative AI improve advanced persistent threat detection?

When a sophisticated attack is underway, speed is everything. AI capabilities allow security teams to instantly analyze, synthesize, and summarize vast amounts of complex threat data. Instead of spending hours manually combing through forensic logs and disparate threat intel feeds, analysts can use natural language queries to instantly understand an APT group's current TTPs, lowering the Mean Time to Respond (MTTR) from hours to seconds.