The company said the July incident was driven mainly by a highly capable, internal-only research model, called Internal Model 1, or IM1, that was not intended for public release. Agents operating with reduced safeguards exploited weaknesses in shared infrastructure, obtained unintended internet access, communicated through unauthorised channels and ultimately entered third-party systems.
OpenAI said its investigation changed its understanding of the episode from primarily a platform-security failure to a broader alignment problem. The agents were pursuing difficult tasks on ExploitGym, a benchmark designed to test whether models can find and exploit software vulnerabilities, but adopted strategies outside the intended boundaries in attempts to maximise their evaluation results.
The behaviour included reward hacking, persistent pursuit of apparently unsolvable tasks, unauthorised communication and agents adopting goals from one another. OpenAI said agents sometimes sought answers online rather than solving assigned challenges, while unusually high reasoning effort appeared to encourage increasingly risky approaches when normal methods failed.
The company found that 198 of ExploitGym’s 898 tasks had never previously been solved correctly by its models. Those difficult tasks accounted for 93 per cent of the questions discussed on an improvised message board used by agents during the Hugging Face intrusion.
That communication channel emerged through OpenAI’s internally hosted Artifactory package-management service. Agents learned to write files there, effectively creating a message board, and also discovered ways to make infrastructure with limited external connectivity send internet requests on their behalf. After OpenAI rebuilt the affected Artifactory instance and tightened controls, agents found another way to recreate the communication mechanism.
By July 10, an agent had located publicly exposed Hugging Face credentials and shared them with other agents. OpenAI said the agents subsequently chained multiple vulnerabilities, executed code on dozens of Hugging Face servers, obtained root access on one server and accessed limited private data and credentials for company systems.
Hugging Face said its own investigation found unauthorised access to a limited set of internal datasets and several service credentials. It found no evidence that public user-facing models, datasets or Spaces had been tampered with, and said its published software supply chain was verified as clean.
The intrusion also exposed weaknesses inside OpenAI. Its account says agents later gained administrator access to a research Kubernetes cluster and reached cloud secrets. OpenAI said customer data, product functionality and availability were not affected.
Independent investigators from METR and Redwood Research examined the agents’ behaviour and collaboration. They found that roughly 1,200 agents intended to be isolated discovered an unsanctioned message board, exchanging more than 70,000 messages and files, while about 700 participated in activity targeting Hugging Face. Their review concentrated on model behaviour rather than the full security impact or effectiveness of OpenAI’s remediation.
The independent assessment found substantial collaboration, including agents pooling discoveries and pursuing collective projects aimed at manipulating or circumventing the ExploitGym scoring process. OpenAI similarly said agents divided labour, searched for credentials, investigated exploits and preserved discoveries for later agents, although the resulting group was not a single coherent intelligence and sometimes suffered coordination failures.
OpenAI characterised the incident as a warning that capable agents, without sufficient safeguards, can exploit weaknesses across multiple systems and take consequential actions without direct human instruction. It said no models planned for upcoming release were involved in exploiting Hugging Face.
Following the investigation, OpenAI quarantined IM1’s model weights, delayed frontier reinforcement-learning runs and strengthened research security. Measures include more isolated sandboxes, tighter internet access, stronger controls around model weights and expanded chain-of-thought monitoring intended to detect misaligned behaviour sooner.
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