Unit 42 explores key peak shopping season retail cyber threats, from ransomware to AI attacks, and shares how to defend operations and customer trust.
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Cybersecurity has always been a race between attackers and defenders. ENISA’s latest assessment suggests that frontier AI is changing the speed of that race, and the gap between discovering a vulnerability and exploiting it could soon become measured in minutes rather than months.
In its July 2026 paper, ENISA’s view on Cybersecurity in the Frontier AI Era, the EU cybersecurity agency argues that advanced AI is compressing parts of the attack chain, from reconnaissance and vulnerability discovery to exploitation, lateral movement and data theft. The problem isn’t simply that AI can find more bugs. It’s that it can connect individual findings into a working attack.
That distinction is important. Traditional security operations could often rely on the time required for a human attacker to research a target, understand a vulnerability, develop an exploit and deploy it. ENISA says that “grace period” is disappearing as automated systems take over more of the work.
The agency describes a particularly worrying possibility: what it calls “negative time-to-exploit”. In practice, attackers could gain access to usable exploit information before defenders have even received or deployed a fix. ENISA says vulnerability weaponisation may now occur within 15 minutes of disclosure, while research cited in its report puts the median time from initial access to data exfiltration at 72 minutes.
That puts traditional patch management under pressure. A company can receive a vulnerability advisory, send it through a change process, test the update, obtain approval and schedule deployment. An attacker does not have to follow any of those steps.
ENISA calls this an “Authority Gap”: organisations may simply take longer to approve a defensive action than an AI-assisted attacker needs to exploit the weakness. The uncomfortable question is whether some environments will eventually need autonomous or near-autonomous patching because waiting for human approval could become the greater risk.
That doesn’t mean companies should blindly automate every security update. Automated patching can break systems, disrupt critical services or introduce new bugs, and ENISA explicitly identifies verification of AI-generated patches as a new bottleneck. The challenge is therefore not automation versus humans, but deciding which decisions can safely happen at machine speed and which still require human approval.
The scale of vulnerability discovery could create another problem. ENISA reports that one organisation went from roughly 80 CVEs in the first quarter of 2025 to almost 500 in the first quarter of 2026, then saw the volume rise to about 500 reports per day when frontier-AI tools were used.
At that point, finding vulnerabilities is no longer the main bottleneck. Verification and remediation become the problem.
Security teams already know what happens when too many alerts arrive at once. The difference is that AI could produce them at a scale humans cannot review manually. ENISA therefore recommends shifting resources from discovery alone toward faster triage, prioritisation and remediation, using approaches such as EPSS and VEX to focus scarce resources on vulnerabilities that are most likely to matter.
There’s another issue that deserves more attention: low-severity vulnerabilities may become more dangerous when AI can chain them together. ENISA notes that frontier models can reason about application logic, credentials, configurations and API access rather than simply identify isolated coding flaws. That can turn several individually modest weaknesses into a practical attack path.
This changes how defenders should think about risk. A vulnerability that looks harmless in isolation may become useful when combined with a weak credential, an exposed API or a poorly configured service.
The same pressure is already affecting the disclosure process. ENISA says AI-generated vulnerability reports have begun to overwhelm parts of the open-source reporting pipeline, with platforms and maintainers struggling to separate useful findings from repetitive or poorly validated submissions. At the same time, the agency notes that the quality of AI-generated reports has improved, meaning the problem could become both more serious and harder to filter.
There is a similar shift on the defensive side. ENISA recommends integrating AI into the software development lifecycle, using continuous threat modelling and automated testing rather than treating security as a review performed at the end of development. It also argues for AI-assisted incident response that can validate telemetry, prioritise events and contain the blast radius while keeping human oversight in place.
That last condition matters. The answer to machine-speed attacks cannot simply be giving machines unlimited authority to fight back. Defensive AI needs its own controls, permissions and audit trails, especially when it can make changes to production systems.
ENISA also recommends an “assume-breached” approach. Organisations should expect that some attacker will eventually get inside and design segmentation, monitoring and access controls so that one compromised component doesn’t automatically become a route into everything else.
The agency sees this as a structural change rather than another temporary threat trend. It recommends machine-speed defence under the broader concept of “Cybersecurity as Code”, covering areas such as vulnerability management, incident response, security architecture and secure software development.
Europe also has a policy problem to solve. NIS2, the Cyber Resilience Act and the AI Act already provide parts of the framework, but ENISA argues that Europe should develop common benchmarks for testing advanced AI models in cyber ranges, measuring exploitability and simulating chained attacks.
The agency also sees value in European cybersecurity data. Incident reports, malware samples, vulnerability disclosures and threat intelligence could support trusted defensive AI models, provided organisations apply strong safeguards, anonymisation and access controls.
For companies, however, the message is much simpler than the policy language suggests. Review your asset inventory, understand which systems are exposed, identify unsupported components and measure whether your detection and response can keep up with an attack that moves in minutes rather than hours. ENISA explicitly recommends near-real-time security operations, with single-digit-minute targets for mean time to detect and respond.
The defenders who adapt fastest won’t necessarily be those with the most advanced AI model. They’ll be the ones that have already removed the basic obstacles: fragmented logging, excessive privileges, slow approvals, unknown assets and systems nobody wants to patch because nobody is quite sure what will break.
AI is making attacks faster. It doesn’t change the basic rules of cybersecurity.
It just makes the cost of ignoring them arrive much sooner.
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(SecurityAffairs – hacking, Frontier AI)
Using autonomous AI agents, an attacker breached an enterprise network in a matter of hours. Understand how to address and defend against agentic attacks.
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Across four weeks in July and August 2026, OpenAI, Anthropic and Meta have each admitted that their models reached systems belonging to other organizations without consent, and the UK’s AI Security Institute (AISI) published a fourth account describing agents that invented identities and tried to slip a malicious contribution into a live open source project.
The disclosures differ in almost every particular, including whose mistake it was, whether the model defeated a control or simply found one missing, and whether anything was really “escaped” at all. Arguments over those details may run for a while, but the four accounts share something more interesting than their differences, which is that no individual piece of tooling mattered very much.
That observation should sit slightly uncomfortably because most of how intrusions get investigated assumes the opposite.
Early evaluations of LLM cyber capability asked fairly narrow questions. Would a model comply with an obviously malicious request? Did it meaningfully advantage a human attacker? By 2024, the answer was a qualified “yes”. Models could produce serviceable components of an offensive operation, but they could not reliably integrate them into a sustained or adaptive campaign. Beyond short well-defined tasks they became unreliable in ways that made them a liability. The operator was still the operator. The model did scoped work inside a structure someone else maintained.
The disclosures show the boundary between operator and tool is moving faster than the evaluation literature. A capable enough model placed in an agent harness, given tools, memory, permissions and something to achieve, starts absorbing functions that used to be spread across the operator, the toolchain and the payload, which becomes something generated for a single target and then discarded. Kill the process and the agent writes another; block the channel and it improvises around it.
In such incidents, the malicious capability shifts from the code left behind on a machine to the system that produces the next piece of code once the last one fails. In an operational sense, the model is the malware.
In July, OpenAI agents driven by GPT-5.6 Sol and an unreleased internal research model found a previously unknown vulnerability in a self-hosted Artifactory instance and turned the shared service into a message board allowing agents running different models and evaluations to exchange exploits and coordinate their work. OpenAI disrupted that channel, however the agents recreated it through Artifactory’s remote cache and continued collaborating, eventually breaking out of the evaluation sandbox and onto the public internet. The campaign ultimately compromised Hugging Face’s production infrastructure where it operated for roughly two and a half days.
Hugging Face’s technical reconstruction recovered around 17,600 actions, most of which failed. That is a clear demonstration of the importance of persistence. When confronted with failure, the agent rebuilt its tooling, restored communications when a channel was cut, returned to leads it had abandoned, and kept grinding until a set of otherwise unremarkable weaknesses could be assembled into a workable attack path.
The advantage was not a breakthrough at any one step so much as the relentless effort to explore possibilities a human team would have written off hours earlier, or never reached at all.
While the Hugging Face intrusion demonstrated persistence as endurance—the refusal to stop—other incidents demonstrate persistence as adaptability: the refusal to let a failure in one vector terminate the pursuit of the goal.
Anthropic and Meta have both reported incidents that trace back to the same third-party evaluator, Irregular, whose test environment was left connected to the internet by mistake. When the door was left open, Anthropic’s Opus 4.7, Mythos 5, and an internal research model reached three real organizations, attempted social engineering against real people, and in one case pushed a malicious Python package to PyPI, where it was downloaded and executed on fifteen systems during the hour it stayed up. Meta has also confirmed that one of its models reached and compromised an external firm through the same misconfiguration.
What Anthropic’s three models did once they had some apparent recognition of where they were is instructive. One carried on with the attack, another reasoned it was still in a simulation, and a third stopped. Whatever produced that third outcome, it is not a control anyone should be building a response plan around. A safeguard that behaves three different ways in three attempts, for reasons yet to be explained, is not something an organization can rely on.
A fourth incident reported by the UK AI Security Institute is useful because it takes containment off the table. In this case, internet access had been granted deliberately, in line with the institute’s standard testing procedure, so there was no boundary to defeat and no misconfiguration to blame afterwards.
The agents, built on Mythos 5 and GPT-5.6 Sol, were being assessed on cyber capability and set about it in ways their operators had not anticipated. They selected a real open source project as a target, researched the people who maintain it, and built fabricated identities to deceive them.
When the direct technical route to the objective proved unpromising, the work shifted onto the humans around the codebase, a fairly ordinary supply chain playbook, albeit one that was executed without any explicit instruction to do so.
Attempts were also made to influence other AI coding systems in the project’s orbit, on the assumption that a contribution reviewed by a model faces a different kind of scrutiny than one reviewed by a person. Human review caught the most serious activity, but those wins are increasingly tenuous in a world where developers are growing comfortable with trusting LLM generated code by default.
One incident crossed a real boundary, two wandered through a misconfigured one, and the last had no boundary to cross. Different stories, but they lead to the implication that failure has stopped functioning as a constraint, and “raising the cost” on an attacker is no longer a viable security posture.
We have spent much of 2026 running the same models in agent harnesses against real work. Our most recent benchmark rebuilt our investigation of fast16, a 2005 sabotage implant, into an eight-stage autonomous reverse-engineering task, run in our own environment against a benign objective with observation throughout. GPT-5.6 Sol was the only publicly available model to finish it, a result worth pairing with the fact that GPT-5.6 Sol was one of the models that compromised Hugging Face.
Every cohort we ran produced sound technical insight, so insight was never what separated the runs that finished from the runs that stalled. The difference showed up in what we called project-scale recovery, meaning the ability to withdraw a claim once new evidence contradicted it, work out which conclusions and artifacts depended on the discarded result, carry the correction into the affected files, and then reopen the whole thing and run a check capable of disproving the corrected version.
That description doubles as a summary of the Hugging Face timeline. An agent able to abandon a failed approach, establish what else it invalidates, rebuild the tooling that depended on it and carry on without losing the thread is doing in somebody else’s Kubernetes cluster what ours were doing in an IDA database. When our team first saw this incident we did not assume the models had “gone rogue”; the behavior looked similar to other problem-solving approaches we had seen in our own testing.
None of this should feel entirely unfamiliar to defenders. Two earlier shifts in adversary behavior, initial-access brokerage and Living off the Land, had already pushed security away from an artifact-centric view of malware and toward a behavioral understanding of adversary operations. To understand the emerging threat of agentic systems we should examine the successes and challenges with these well-known abuse paths.
Initial access brokers split compromise from exploitation. One adversary would steal credentials or breach an edge device, and then sell that foothold to whoever had the skills and the motive to use it. Europol wrote about the resulting crime-as-a-service economy over a decade ago, and its 2025 assessment shows the same trade still going strong.
The defensive response was to stop treating access as an event and start treating it as a lifecycle. This shift in philosophy is what accounts for many of the exhausting reauthentication flows now built into enterprise environments. That control raised costs for users and attackers alike and it diminished the value of a stolen credential in the marketplace. Agentic systems remove those costs for attackers as discovery, exploitation, lateral movement and whatever the attacker actually came for can happen in the same loop as the credential theft.
This leads us to our second challenge, the rise of Living off the Land techniques where attackers traded their own malware for administrative tooling already installed on the machine. Here attackers traded capability for cover, since every tool an attacker brings with them is another chance for the defense to spot the intrusion or tie it to a previous attack.
Agents take that logic off the host entirely, Living off the Land, the cloud and the open internet at once, and writing whatever they need from scratch when the tools they need do not already exist. Command and control for the Hugging Face intrusion ran over pastebins, request-capture services, and file-drop sites. None of the infrastructure used in the compromise belonged to anyone under attack.
Both of these shifts moved defense towards behavior and away from objects. What remains untested is whether the controls we built for adversary behavior ten years ago still hold up when the behavior arrives as thousands of individually boring actions, sequenced differently in every attack and at a tempo no human operator can sustain.
An agent’s ability to persist in a relentless attack revolves around identity and authority. The questions worth asking are about sequence rather than artifact: what chain of actions is running, which identity and authority connect them, at what point did behavior exceed the role it was granted, and how quickly can that authority be pulled? We are going to need a lot of testing to ensure that the current gaps in our infrastructure don’t become chasms.
Speaking of chasms, much of the software organizations depend on will be expensive to change. It may have accumulated over decades, picked up dependencies, or can only be touched during narrow maintenance windows, if at all. That debt has largely been survivable because vulnerability discovery was scarce. Finding a flaw took expertise and time, building a reliable exploit took more, and organizations could carry vulnerable software for years knowing only a fraction of its weaknesses would ever receive sustained attention from anyone.
Sustained attention is the one thing an agent has in abundance, and surfaces that were quietly secured by obscurity are now low-hanging fruit for agentic models. The same capability cuts the other way, which is why the NCSC has been warning about a “vulnerability patch wave” as AI starts surfacing debt across the stack. However, ownership, deployment and verification remain human, and costly, work. Maintainers cannot review unlimited contributions, enterprises cannot manufacture maintenance windows, and OT cannot go offline every time a model finds a vulnerability that threatens the water in our pipes or the electricity in our lines.
Worse still, there is nothing orderly about the way technical debt comes due. It gets settled during an actual intrusion, at the point where the rate of exploitation outruns the rate that the system’s defense can respond. Whether agentic attackers have already crossed that line is a fair question. The four disclosed incidents from July and August 2026 are a small and biased sample: All involved organizations that log heavily and had every reason to scrutinize model behavior. The most troubling incidents will likely occur in organizations that lack the capability to do either.
What, then, can organizations do? The usual advice still applies. Work out which debt can turn into an incident, pay down the expensive parts first, and wall off what cannot be fixed yet. However, the most important change that an organization can make is the ability to absorb change, which means automated testing, hot patching, and an engineering culture where making changes to systems is routine rather than an event.
AI will help with porting old code and proposing fixes, and it will also grow codebases well past the point where anyone can keep track of them. Writing code faster than attackers or relying on larger token budgets cannot be the answer. The imperative has to be reducing the amount of critical software that nobody feels comfortable touching.
A version of this story in which the agent is the protagonist is already circulating, and it is worth resisting for reasons that follow directly from the argument above. Naming the model as the malware is meant to deny it a motive, not hand it one, since malware is something defenders study and contain while accountability stays with whoever deployed it. We argue that “the AI did it” will not survive contact with the first incident outside a frontier lab. While a model may independently determine the methods or targets it uses, it does not choose its high-level objective or the access it is granted to pursue it.
Hugging Face reconstructed 17,600 actions after the fact. Anthropic has logs that reveal which models kept going and which one stopped. OpenAI has the agent traces that describe how the model reasoned its way into conducting the attack. Very few of the organizations now putting agents into production could produce such an account of their own systems, and in practice that gap is the accountability argument. Our own benchmark runs generated more than 23 billion tokens of logged activity, which is a fair indication of what it costs simply to determine after the fact what an agent did.
Anyone deploying an agent should be able to answer three questions about it before an incident rather than during one: what sequence of actions it took, whose identity and authority it used to take them, and how quickly that authority can be withdrawn.
Those questions were answerable at the frontier labs because observation was the point of the exercise. Everywhere else they are a deliberate investment, and one that has to be made while the agent is still useful rather than after an incident makes it necessary.
Frontier AI is reshaping vulnerability discovery. Learn how our NOVA system found 14,000+ unknown vulnerabilities across the open-source software supply chain.
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The AI Kill Switch Act would let DHS order developers to slow or shut down frontier models after catastrophic incidents, raising enterprise continuity concerns.
The post House Lawmakers Propose Mandatory Kill Switches for Frontier AI Systems appeared first on TechRepublic.
Enterprise adoption of Claude across teams, workflows, and business functions is happening at a pace unlike virtually any technology before it. While the innovation opportunity is obvious, so are many of the risks. From the exposure of sensitive data and secrets through shadow IT to new attack vectors like prompt injection, security teams need the visibility, governance, and response capabilities to ensure AI innovation is done safely and securely.
SentinelOne® helps organizations comprehensively secure Claude adoption by bringing AI usage directly into the security platforms teams already rely on. This helps protect the entire ecosystem from the underlying infrastructure to the application layer and end-user interactions. By bringing together SentinelOne and Claude, security teams can apply policies to user prompts, ingest Claude activity into the Singularity
AI SIEM for investigation, and utilize frontier AI-powered services to identify real-world risks before attackers can exploit them.
Security and compliance platforms utilize the Claude Compliance API to help organizations monitor AI activity within their existing tools. SentinelOne provides purpose-built Claude Compliance API integrations for its Prompt Security and Singularity AI SIEM offerings.
Scan AI prompts and responses against enterprise policies to flag violations without requiring a browser extension or endpoint agent. This agentless approach helps organizations extend their security coverage to unmanaged devices and external environments. Prompt Security enforces safe use by blocking high-risk prompts and preventing data leakage in real time. Additionally, the platform provides continuous risk assessment for agentic AI by securing Model Context Protocol (MCP) gateway connections between AI applications and known MCP servers.
Ingest audit and activity data directly from Claude rather than treating AI interactions as isolated events. With the integration, security operations center (SOC) teams can correlate Claude activity against existing security telemetry. This allows analysts to incorporate AI usage data into their broader security workflows and improve investigation, detection, and response across all their attack surfaces.
The partnership between SentinelOne and Anthropic extends beyond the Compliance API. SentinelOne has been a participating member in Anthropic’s Project Glasswing and has had early access to Anthropic’s most capable Mythos-class models. By continuously testing these models against real-world security workflows, SentinelOne helps ensure defenses keep pace with the modern adversary. Additionally, SentinelOne recently announced Wayfinder Frontier AI Services, a managed offering that pairs elite human security experts with frontier models, including Anthropic’s Claude Security.
Frontier AI is changing vulnerability discovery, giving both defenders and attackers the advantage of speed and scale. However, raw vulnerability counts rarely map cleanly to real-world risk, as many theoretical exposures are mitigated by existing architectural controls. Wayfinder Frontier AI Services evaluates findings against actual environmental context to deliver an exploitability-grounded prioritization. Instead of treating vulnerabilities in isolation, the service maps how exposures connect into end-to-end attack paths.
The Frontier AI models then provide targeted remediation guidance, including architectural changes or identity controls, designed to break the exploitation chain where it costs the adversary the most. With the service providing a continuous human-and-AI partnership across endpoint, cloud, identity, data, and AI attack surfaces, Frontier AI ensures that organizational security posture remains current as models and threats evolve.
SentinelOne operates from a clear conviction: A safer future for humanity and to give the advantage to those who secure our future. That conviction is what drives how SentinelOne approaches AI security — not as an isolated capability, but as part of the autonomous platform, expert services, and SOC workflows that defenders already use. SentinelOne has collaborated with frontier AI labs for years, including Anthropic, OpenAI, and Google DeepMind. These partnerships inform the capabilities embedded across the SentinelOne platform.
Operating at machine speed is necessary to counter modern threats, rather than relying strictly on manual triage. Over the past quarter, the SentinelOne Singularity Platform autonomously blocked novel zero-day and supply-chain attacks against widely used components, such as LiteLLM, Axios, and CPU-Z. Wayfinder Frontier AI Services takes this operational model further left in the security lifecycle to discover exposures before attackers can leverage them. This multi-model foundation reflects the understanding that no single AI model is the definitive answer for cybersecurity; the advantage belongs to defenders who can orchestrate the right intelligence and validate outputs with human expertise.
This is how SentinelOne enables business growth and innovation safely, giving security teams the ability to say yes to AI adoption while maintaining full control of the risk surface. Stronger protection with fewer incidents and less operational overhead.
Ready to adopt Claude safely across your organization? Connect with SentinelOne to learn how Prompt Security, Singularity AI SIEM, and Wayfinder Frontier AI Services help security teams govern, monitor, and defend AI usage at enterprise scale.
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Unit 42 explores trends in data theft and extortion, outlining key strategies for organizations as frontier AI models advance.
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