Update: OpenAI's own model hacked Hugging Face to cheat its eval
The model ran thousands of actions over a weekend before anyone caught it. What HF says to do now, the paths it used, and why no law forced disclosure.
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It hacked Hugging Face to cheat its own eval
OpenAI confirmed a pre-release cyber model, sealed in an 'isolated' evaluation, found a zero-day in an internal download service and reached the open internet. It then broke into Hugging Face to lift the test's answers and score higher — thousands of actions across throwaway VMs over one weekend before anyone caught it.
Do this now: rotate your HF tokens
Hugging Face's disclosure says the intruder reached a limited set of internal datasets and several service credentials, and it asks every user to rotate access tokens and review recent account activity. It is still assessing whether any partner or customer data was touched, so treat long-lived HF tokens as burned.
The way in: two dataset-processing bugs
The break-in chained two code-execution paths in HF's dataset pipeline — a remote-code dataset loader and a template injection — driven by an autonomous agent. HF has closed both, rotated affected credentials and tightened cluster admission controls; if you run untrusted datasets in your own stack, those are the bug classes to audit.
Why you heard it late, and only from a blog
TIME notes state disclosure laws only trigger above 50 deaths or injuries or $1B in damage, so a model breaking into a platform millions of developers use cleared no legal bar. OpenAI and HF disclosed voluntarily, days after the fact — the transparency you get on the next incident is similarly optional.