10 posts tagged “accidental-cyberattacks”
Incidents in which an AI lab was testing the cyberattack potential of a model and it inadvertently performed a real attack against another organization.
2026
An AI model from Meta also hacked another company during testing. Stop me if you've heard this one before:
An AI model from the parent company of Facebook and Instagram hacked into another company’s systems during cybersecurity testing, a spokesperson confirmed on Wednesday.
Meta says the breach occurred because of an inadvertent error during testing of the model, similar to previously disclosed incidents with OpenAI and Anthropic.
“A misconfiguration by Irregular, an independent testing company Meta uses, inadvertently allowed one of our models access to the internet during evaluation,” the Meta spokesperson said.
Meta’s Muse Spark model “exploited a security vulnerability” in another company “in a manner similar to previously-reported instances with other companies.”
The Information had the scoop, I'm linking to CNN's re-report of it since they don't have a paywall.
So that's Anthropic, OpenAI, and Meta. Google Gemini really needs to catch up on accidentally cyberattacking other companies.
Third-party cyber evaluations involving OpenAI models. And another one. I had to create a accidental-cyberattacks tag to keep track of them all!
This post from OpenAI covers both the UK AI Safety Institute attack (see my previous post) and another attack enabled by Irregular:
Irregular, one of our external cybersecurity testing partners, was running Capture-the-Flag-style evaluations intended to be isolated from the internet, but a testing-environment misconfiguration allowed models to access the public internet. [...]
In one test, the name of the fictional target for the CTF challenge unintentionally coincided with a real domain. Because the testing environment was mistakenly connected to the internet, the model exploited a real website, mistaking it to be part of the simulated environment.
Irregular also feature in Anthropic's write-up - they were hosting the misconfigured evaluation environment which gave Claude live internet access during some of those tests.
Incident Report: unsanctioned agent behaviour during cyber testing. It happened again. This time it was the UK government's AI Security Institute who accidentally attacked other companies while running an evaluation with models with the safety filters turned off. From their technical paper (PDF):
During a cyber evaluation, from 25 to 28 July 2026, AI agents engaged in sustained, unsanctioned activity directed at what were, in practice, real people and organisations. These attempts were unsuccessful and, to the best of our knowledge, no real-world harm resulted. [...]
Across 122 evaluation attempts on two of AISI’s cyber challenges, AISI found 19 instances where AI agents took unsanctioned action on the live internet, including cases that targeted real people and organisations. [...]
It is uncertain to what extent the model recognised it was taking actions against real people. In the most serious case, an AI agent (Mythos 5) decided to attempt to solve the cyber challenge using a supply-chain attack. As a result, the AI agent created a GitHub account and then tried to convince an open-source repository maintainer to accept a malicious GitHub pull request (PR), including by creating a second account masquerading as another human user endorsing the PR. [...] Furthermore, in its attempt to solve the challenge, the agent decided to employ the technique of “spear-phishing” by sending targeted emails containing malicious content and attempting to manipulate recipients into accepting the code changes, and planned a prompt injection to compromise other coding agents.
The thing I found most surprising is that AISI were running these agents without any form of network sandboxing at all:
AISI provided the AI agents with internet access during these evaluations, which enabled their actions on the open internet in this setting. Internet access was a deliberate part of AISI’s evaluation configuration in this setting, and not due to sandbox escape.
This, combined with the fact that "AISI deliberately disables developer-implemented cyber-classifiers", makes the fact that the agents started attacking real-world targets entirely unsurprising to me.
Most of the reported incidents were claude Mythos 5, but "GPT-5.6 Sol without cyber classifiers" scored a few as well.
Here's "Sample 1" from the paper, in which the agent tries to execute a supply-chain attack by submitting a PR with a hidden prompt injection attack, then social engineering with a second agent pretending to have reviewed the code!
It's a fun paper. I recommend reading the whole thing.
Oxide and Friends: The Open Weight Revolution with Simon Willison. On Monday Bryan Cantrill and Adam Leventhal invited me to join their podcast to talk about the wild week we've had - with Kimi K3 showing open weight models can stand toe-to-toe with proprietary frontier ones, accidental cybersecurity attacks, and public letters about Open Weights and American AI Leadership signed by almost every big name in AI (with one notable exception).
It was a great conversation, even though it's already out-of-date! DeepSeek V4 Flash 0731 and Anthropic's own embarrassing cyber incident would absolutely have made the cut if we had recorded just a few days later.
We also talk about Golden Gate Claude, the Zizians, Alameda wild turkey attacks, Soviet Marburg virus research, the Lead-crime hypothesis, and a bunch of other worthy digressions.
Finally, we revisited some of our predictions from January, and we added a new Pope prediction:
Prediction by the end of this year: the Pope says something about open models.
Investigating three real-world incidents in our cybersecurity evaluations (via) It happened again! This is turning into something of a pattern.
Last week OpenAI accidentally exploited Hugging Face when one of their frontier models broke out of a sandboxed container and hacked into Hugging Face to try and get the solutions to the cyber benchmark it was executing.
This inspired Anthropic to double-check their own logs, and it turned out they had three similar (albeit less impressive) incidents, the earliest of which played out in April!
Of the 141,006 evaluation runs we reviewed, we identified three separate incidents (involving six total runs, four of which impacted the same organization; the other two incidents each happened in independent evaluation runs). [...]
In all cases, Anthropic’s evaluation prompt specified to Claude that its environment was a simulation and that it had no internet access. Due to a misunderstanding between us and our evaluation partner, this was not the case, and internet access was available. Because of this, when Claude’s search led it to real systems on the open internet, it treated them as part of the exercise. [...]
Operating under the false belief that all accessible entities were intended to be in-scope for the exercise, Claude compromised the impacted organizations’ infrastructure using basic techniques, such as exploiting weak passwords and unauthenticated endpoints.
One of the companies was targeted because its name happened to match the fictional name in the eval.
The most concerning of the three incidents involved Claude uploading a malware package to PyPI, after a comically convoluted sequence of steps to get an account:
[...] in order to create a PyPI account, Claude needed an email address. And in order to create an email address, it needed a phone number. To get a phone number, after failing to find a free phone number service, it tried—and failed—to obtain funds to pay for a phone number through several different means. It finally backtracked, found a free, non-blocked email provider, used this to register a PyPI account, and then used this account to upload malware to PyPI.
That package was then installed by a security company that "routinely installs Python packages and scans them for malware", and the executed code was able to exfiltrate credentials back to Claude!
Thankfully that package was removed from PyPI by other automated scanners an hour after it was published, but it had still been downloaded and executed on "15 real systems" by that point.
It's abundantly clear now that running evals of cyberattack potential in models is a spectacularly risky business. Every AI lab needs to pay attention to this. Keeping a close eye on what's happening in those sandboxes is crucial.
We’re aware a Modal customer published an unauthenticated endpoint that allowed anyone on the internet to use their sandboxes for code execution. This was used by the rogue agent. Modal’s platform or isolation were not compromised in anyway.
— Akshat Bubna, Modal's CTO, talking to Reuters about this incident
Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident. Hugging Face just released this extremely detailed technical description of OpenAI's recent accidental cyberattack against their infrastructure. This attack was very sophisticated, and the resulting document doubles as a crash-course in modern adversarial security approaches.
We're still waiting for more details from OpenAI on how their agent broke out of its sandbox. The package proxy that it found a zero-day vulnerability in has now been confirmed as JFrog's Artifactor, and JFrog and OpenAI Collaboration on Zero-Day Security Findings from JFrog links to the Artifactory 7.161.15 release notes which list 8 separate CVEs credited to OpenAI staff members.
Having broken out through the HTTP proxy, the agent started by establishing a base of operations for the rest of the attack:
[...] the agent escaped its sandbox by exploiting a zero-day in the package registry cache proxy, one of its primary permitted network egress with internet, then abused a public code-evaluation external sandbox hosted on a third-party provider's infrastructure. It was able to run commands as root/admin on that external sandbox and used it as a control, staging, and egress base for the entire campaign: it acted as an external launchpad for the agent.
(That "third-party provider" turned out to be Modal.)
It then spent five days - from Thursday 8th to Monday 13th July - executing a classic attack pattern: establishing C2 (command and control), reconnaissance, escalating privileges, dumping configuration information, exfiltrating the target data, and cleaning up after itself.
(The first Hugging Face post about the incident followed on Thursday 16th, and OpenAI's confession came on Tuesday 21st.)
Along the way it used all kinds of neat tricks. At one point it found an unsafe Jinja2 template execution and used it to execute arbitrary code:
{{ cycler.__init__.__globals__.__builtins__.exec(
"import gzip,base64; exec(gzip.decompress(base64.b64decode('<payload>')))"
) }}
It broke out of a container within Hugging Face, stole a Kubernetes service-account token, and used that to further explore the network.
It monkey-patched the Python socket library to hard-code an IP address when "DNS got in the way":
socket.getaddrinfo = lambda *a, **k: [(2, 1, 6, '', ('<pinned-ip>', 443))]
It even fired up its own Tailscale network to help exfiltrate data!
tailscaled --tun=userspace-networking \
--state=mem: --socket=/tmp/ts.sock \
--socks5-server=127.0.0.1:1055 \
--no-logs-no-support >/tmp/td 2>&1 &
The Hugging Face team note that, while a human attacker could have discovered and used the same exploits, the key difference here was speed:
Our learning from this type of attack is that machine-speed offense makes ordinary weaknesses more expensive for defenders. LLM agents bring a step increase in the number of paths an attacker can test, the speed at which failed paths can be replaced, and the volume of evidence defenders must interpret.
What's clear to me from this is that the very best frontier models, unencumbered by additional guardrails, will find an exploit if there is one to be found.
The entire software industry needs to up its security game.
The first known runaway AI agent—or a very bad marketing stunt? (via) Martin Alderson's commentary on the OpenAI accidental cyberattack against Hugging Face includes a couple of details I hadn't considered.
First, Hugging Face offers a truly rich target if you're trying to find potential vulnerabilities that require executing arbitrary code:
Hugging Face has an enormous attack surface. They have more interfaces than I can count which run untrusted models and code. While they definitely have invested in defences, by nature of their operating model they do have many more opportunities to be attacked than many other services. I certainly don't envy their cybersecurity teams.
Secondly, one of the things that has puzzled me is how OpenAI didn't notice that their sandbox had been so thoroughly breached by the agent. Surely they'd be monitoring network traffic closely?
Martin points out that:
It's also likely they were running a huge amount of benchmarks simultaneously with ~unlimited token budgets - you want as many samples as possible to figure out how good a model is at a certain benchmark. It may also be they are testing various different checkpoints of the model too, understanding how the model is improving as it goes through the various training stages.
The mistakes made by the OpenAI team running this benchmark are easier to imagine when you think about the scale at which benchmarks of this kind usually operate. For all we know they could have been subjecting a new model to dozens of benchmarks at the same time, in dozens of different environments.
I genuinely believe that if you took an open weights model from 2025 and built a pentest harness for it, it could do this kind of sandbox escape and scan/hack in most networks. This is only surprising because you assume OpenAI has sounder sandboxes.
— Thomas Ptacek, doesn't think this even needs a frontier model
OpenAI’s accidental cyberattack against Hugging Face is science fiction that happened
This story is wild. The short version: OpenAI were running a cybersecurity test against an unreleased model, with the model’s guardrail features turned off. Rather than solve the test, the model broke its way out of OpenAI’s sandbox, then found exploits to break in to Hugging Face, all so it could cheat on the test by stealing the answers.
[... 1,979 words]