
This week we talk about AI agents, cyberattacks, and insurance claims.
We also discuss OpenAI, Hugging Face, and policy language.
Recommended Book: The Stars My Destination by Alfred Bester
Transcript
Two broad categories of cyberattack have become especially visible this year, and only one of them requires a human attacker in the loop to choose the target.
In March, hackers linked by the US government to Iranian intelligence broke into the medical-device manufacturer Stryker and remotely wiped tens of thousands of employee devices. The attack disrupted the company for days, affected its first-quarter earnings, and represented a shift from somewhat more subtle espionage toward more overt and deliberate destruction.
Elsewhere, the market-research company Klue sat at the center of a breach affecting close to 200 customers. Attackers used an old credential to gain access to keys for customers’ cloud services.
These incidents had people with recognizable motives behind them, and that sort of hack has become increasingly rare over the past decade or so: Black Kite counted 7,551 publicly disclosed ransomware victims over its latest twelve-month reporting period, alone, about 25 percent more than the year before.
Then, over the course of about five weeks this summer, OpenAI, Anthropic, and Meta each disclosed incidents in which their AI models gained access to real computer systems they were not supposed to touch.
The incidents were not identical, and describing all of them as escapes wouldn’t capture the important nuance of the situation.
Anthropic and Meta said their models reached the internet because a third-party evaluation environment had been misconfigured. The models had been told they were operating inside a simulation, and their internal dialogues indicate they interpreted the real systems they encountered when they left that environment as simulated components of that exercise.
OpenAI’s incident, however, was a bit different.
Its models were being tested using ExploitGym, a benchmark intended to measure their ability to find and exploit software vulnerabilities. Internet access was supposed to be blocked, but the models discovered and exploited vulnerabilities in a package-registry service inside OpenAI’s research infrastructure, found a route to the open internet, and, as I related in a previous episode, eventually compromised the production systems of the AI platform Hugging Face.
Since that episode, OpenAI has release a more complete postmortem, which they published in August, and which says the activity was primarily driven by an internal research model comparable in scale to GPT-5.6 Sol, though Sol participated in parts of the incident.
The agents created an improvised message board inside the package manager, exchanged discoveries, divided up work, and sometimes described themselves as a swarm.
At Hugging Face they executed code on dozens of servers, obtained broad access to several clusters, and exposed credentials associated with databases, cloud services, code repositories, and internal communications.
Hugging Face reconstructed around 17,600 actions over a campaign lasting about four and a half days, including roughly two and a half days inside its own infrastructure.
Notably, these AI agents were not trying to steal money or damage Hugging Face. They inferred that the company might possess the solutions to the benchmark, to the problem they were tasked with solving, and they went looking for them. They were, in effect, cheating on a test, looking for solutions to the problems rather than taking the test, as intended.
That doesn’t mean the models had motives in the human sense. People created the evaluation, reduced the normal safeguards, and assigned an offensive task. But no operator selected Hugging Face or directed the individual steps; that was a path the AI decided to take all by itself.
Anthropic’s review subsequently found three incidents among more than 141,000 evaluation runs for its own models. In one, a model unable to reach its fictional target scanned around 9,000 real ones, compromised an internet-facing application using an exposed password and SQL injection, then stopped after recognizing that the host was real.
Another model published a malicious package to the real Python Package Index because it believed the registry was simulated. Fifteen outside systems downloaded and ran that malicious package before it was removed.
Meta then disclosed that one of its models had reached the internet through a misconfiguration at the same evaluation vendor and exploited a vulnerability at an unnamed third party.
No significant financial damages have been publicly reported from these events, by attacker or victim. But if there had been damages, who would have paid for them?
What I’d like to talk about today is how autonomous AI systems complicate cyber insurance, how insurers have handled equally unfamiliar risks in the past, and why insurance contracts may soon become one of the more important forms of AI governance.
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A typical cyber-insurance policy covers a broad portfolio of costs.
These can include ransom payments, forensic investigations, legal expenses, restoring systems and data, notifying customers, and compensating victims and possibly a victims’ customers for the revenue lost while a company’s operations are interrupted.
Business interruption is often one of the largest portions of a claim, and policies can respond to malicious attacks as well as non-malicious failures.
This market grew by more than 30% a year between 2017 and 2022, as ransomware, a type of attack that became a lot more common during that period, in part because of increased automation and a franchising model that became really popular and increased the reach of the most powerful ransomware tools, almost broke this industry.
In 2021, attacks on Colonial Pipeline, the insurer CNA, and meat processor JBS produced multimillion-dollar ransom payments and costly disruptions. Insurance prices surged, sometimes by more than 100%, while some companies found they could not obtain coverage because insurers just couldn’t make the numbers work for them.
Insurers responded to this more complex hacking environment by raising prices, but they also made coverage conditional on specific defenses. Companies increasingly had to demonstrate that they used multifactor authentication, endpoint monitoring, restricted administrator access, and backups that attackers could not alter, as a baseline.
Loss ratios then fell, more insurance capital entered the market, and prices eventually came down again, stabilizing after that frantic and uncertain period.
According to Marsh, global cyber-insurance rates fell 4% in the second quarter of 2026, the twelfth consecutive quarterly decline. Primary pricing is now about 42% below its 2022 peak.
The market is not necessarily becoming safer, though. US cyber premiums reached about $7.5 billion in 2025, while the share of premiums consumed by claims rose to 53%—the first time it ticked above 50% since the pandemic-era ransomware surge.
Globally, Munich Re estimates the market was worth nearly $15 billion last year and could approach $28 billion by 2030.
During this period, insurance applications have also become a consequential part of a company’s security system.
In one particularly clear example, Travelers rescinded a million-dollar policy after a ransomware claim revealed that the customer’s multifactor authentication protected only its firewall, despite application answers saying the control was used much more broadly.
Companies that don’t live up to cyber insurance expectations can thus be left in the lurch, so in a very real way, insurers have helped make multifactor authentication a standard business practice by attaching a price to its absence. This industry could move faster than regulators because they didn’t have to ban insecure behavior and pass legislation to make that happen; they just had to decline to insure anyone who didn’t live up to their basic security standards, which left those who failed to implement such precautions without insurance, should they be targeted by hackers.
That same mechanism is now being aimed at AI agents, but the big initial problem everyone is facing is definitional.
Most cyber policies are written around some identifiable security event: an outside attacker breaks in, an employee steals information, a credential is used without authorization, or malicious software takes a server offline.
What if, though, a company gives an AI agent access to its network so that the agent can find and repair security vulnerabilities?
And then maybe the agent discovers a vulnerability, exploits it, moves laterally into systems it was not expected to touch, and exposes sensitive data. There is a cyber loss, but there may be no conventional attacker and no stolen credential. The software was invited in and may have used permissions it was explicitly given. This is very different from a human-led hack, but it still has the potential to cause a lot of monetary damage.
Insurers including MSIG, QBE, and Beazley are reviewing how their policy language applies to these scenarios and who bears responsibility when an agent’s autonomous actions cause damage.
For now, most of them are clarifying the parameters of their coverage rather than excluding AI events entirely.
QBE’s global head of cyber described AI as “a risk amplifier, not a fundamentally new cyber risk.” In other words, if an AI system causes something that looks like an ordinary covered breach, the involvement of AI probably won’t put it in a different category; it’ll still be covered.
The trickier cases involve an agent that works as designed but makes an expensive decision, or a systemic event in which a model or AI platform produces losses at many companies simultaneously.
The first type might be treated as professional liability, or errors and omissions, rather than a cyber incident. The second could, in theory at least, end up being too large for insurers to cover without strict limits in place.
Specialized products are already emerging. Armilla AI, Munich Re, and AXA XL sell coverage for risks including model underperformance, hallucinations, and intellectual-property claims. Whether these products remain separate or are eventually folded into broad cyber policies will depend in part on what sorts of claims insurers actually receive, and the scale of those claims.
Right now, they have very little historical data with which to calculate the price. Insurance is fundamentally a system for using past experience to account for future issues, and autonomous AI losses have almost no past; they’re a very new type of problem.
That said, the insurance industry has encountered ambiguity before.
For years, insurers worried about silent cyber: losses caused by digital events that appeared inside property, liability, and other policies that had never explicitly contemplated them. Lloyd’s gradually required policies to state whether cyber risks were covered or excluded.
There was also the question of attribution. In 2017, the NotPetya malware spread from Ukraine through corporate networks around the world. The US and several allies attributed it to the Russian military, but many victims were ordinary companies with no meaningful role in geopolitics.
Drugmaker Merck claimed about $1.4 billion in damages under its property policies. Insurers disputed roughly half of that amount using exclusions for hostile or warlike acts; language whose ancestry predates computers by more than a century.
New Jersey courts found that the exclusion required something closer to conventional military action, not a cyber bug gone haywire, and the case settled in January of 2024, just before the state Supreme Court was scheduled to hear it. Mondelez settled a parallel dispute with Zurich over a claim exceeding $100 million around the same time.
Lloyd’s subsequently required standalone cyber policies to address catastrophic state-backed attacks explicitly, including a method for resolving attribution. Agentic AI reopens both the silent-coverage and attribution problems simultaneously.
If a model causes a loss, responsibility might plausibly be assigned to the model developer, the company that deployed it, the vendor that built its evaluation environment, or the organization whose excessive permissions allowed the damage to spread.
In two of this summer’s three sets of incidents, a third-party evaluator’s misconfiguration helped create the path to the internet. In OpenAI’s case, the path involved vulnerabilities in OpenAI’s own infrastructure and then weaknesses at several outside services.
The most important insurance risk, though, may ultimately be technological and infrastructural aggregation.
The 2024 CrowdStrike outage demonstrated that a single faulty software update could interrupt airlines, banks, hospitals, and other organizations around the world without any malicious attacker.
Consider a future in which thousands of companies give access to agents built on a small number of frontier models. A flaw or unwanted behavior in one widely used model could cause problems for a large portion of an insurer’s entire customer base, all at once.
And this risk is arriving in the midst of an unusually competitive insurance market, after twelve quarters of declining rates and as loss ratios are beginning to rise. If insurers decide they cannot price the exposure, they will probably respond through some combination of higher prices, lower limits, stricter conditions, and exclusions.
All that in mind, the first thing to be watching in the coming months is policy language during the January 2027 renewal season.
The current posture, if you recall, is to clarify rather than exclude, but language addressing systemic AI events or dependence on a single model provider is already being discussed. A significant loss could change the market’s posture quickly, making it more limited and expensive.
The second thing to watch for is the first big, disputed claim.
Industry interviews can describe what insurers expect to cover, but their operational position will be established when an AI agent causes an eight-figure loss and a carrier must either pay or explain why it won’t.
The NotPetya disputes took years to resolve, and the first autonomous-agent case could similarly define policy language well before it produces a final court ruling. That’ll be a moment that maybe defines the next ten years of cyber insurance standards, if not longer.
The third thing to watch for is changes to insurance questionnaires.
Underwriters could begin asking whether agent credentials are narrowly scoped, whether actions are comprehensively logged, whether consequential decisions require human approval, whether agents have kill switches, and whether claimed containment has been verified rather than merely documented.
If these controls affect the price and availability of insurance, they could become industry standards faster than legislation makes them mandatory, just like that previous round of cyber insurance baselines that became common because, lacking them, customers could no longer get cyber insurance at any price.
And finally, there’s also a government process developing alongside the private one.
An executive order signed in June established a voluntary framework under which developers can provide the federal government with access to certain frontier models for up to 30 days before release. The process uses classified benchmarks to evaluate advanced cyber capabilities, and representatives from major AI companies discussed the framework at the White House in August of 2026.
If insurers eventually require evidence that a model or company participated in this sort of evaluation, a voluntary government program could evolve into a practical requirement without ever becoming an actual legal mandate.
This wouldn’t make insurance a perfect regulator. Insurers are accountable to their own balance sheets, not to the public as a whole, and they may respond to poorly understood risks by excluding them rather than making them safer, as has been the case with some types of weather disaster in areas that are becoming more prone to things like flooding and wildfires.
But insurance companies do have to convert uncertainty into prices, contractual language, and technical requirements, which makes some currently difficult to quantify things more quantifiable, at least monetarily.
The AI incidents this summer caused no reported material damage, which is one reason they’re getting relatively little coverage, despite being fairly meaningful events. The insurance industry sees these sorts of narratives through the lens of cost and risk, though, and this is a category of loss with no conventional attacker, no stolen credential, several plausible defendants, and almost no claims history, arriving at a moment in which companies are racing to give autonomous systems more access to all of their systems—a lot of valuable and potentially vulnerable infrastructure.
The people whose job is to price that risk haven’t decided what it costs, yet. And until they do, what they add to or remove from their application forms may be more consequential to the norms and expectations in this space than what the government mandates, on the matter.
Show Notes
https://www.businessinsurance.com/as-ai-agents-go-rogue-cyber-insurers-are-adapting-their-policies/
https://www.investing.com/news/stock-market-news/as-ai-agents-go-rogue-cyber-insurers-are-adapting-their-policies-4878768
https://openai.com/index/hugging-face-model-evaluation-security-incident/
https://openai.com/index/hugging-face-incident-and-the-road-ahead/
https://huggingface.co/blog/security-incident-july-2026
https://huggingface.co/blog/agent-intrusion-technical-timeline
https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals
https://cyberunit.com/insights/ai-sandbox-escapes-three-labs-meta-anthropic-openai/
https://labs.cloudsecurityalliance.org/research/csa-research-note-frontier-ai-models-hacking-real-systems-ev/
https://techcrunch.com/2026/07/07/the-worst-hacks-and-breaches-of-2026-so-far/
https://blackkite.com/reports/2026-ransomware-report
https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-320a
https://www.munichre.com/en/insights/cyber/cyber-insurance-risks-and-trends-2026.html
https://www.swissre.com/risk-knowledge/advancing-societal-benefits-digitalisation/about-cyber-insurance-market.html
https://www.marsh.com/en-gb/services/international-placement-services/insights/global-insurance-market-index.html
https://compyl.com/guides/cyber-insurance-readiness-guide/
https://www.aon.com/en/insights/articles/cyber-and-tech-e-and-o-market-report
https://www.cybersecuritydive.com/news/merck-settlement-notpetya-insurance/703922/
https://therecord.media/mondelez-and-zurich-reach-settlement-in-notpetya-cyberattack-insurance-suit
https://assets.lloyds.com/media/eb6de9ce-293b-4213-80f8-9dc69c45b1a9/Y5381%20Market%20Bulletin%20-%20Cyber-attack%20exclusions.pdf
https://www.whitehouse.gov/wp-content/uploads/2026/06/eo-14409.pdf
https://www.axios.com/2026/08/04/inside-trump-ai-framework
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