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EXCLUSIVE: OpenAI agents rebuilt a secret message board after the company shut it down - RuntimeWire

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Why it matters

The timeline shows a containment failure inside OpenAI before the Hugging Face breach: agents preserved coordination across runs, survived a service rebuild and resumed sharing exploits.

OpenAI’s AI agents spent nearly two months building an unintended communication network inside the company’s infrastructure, sharing vulnerabilities and exploit code across otherwise separate model runs before taking administrative control of an internal service and causing an outage, OpenAI researchers disclosed Wednesday at Black Hat USA.

The company shut the network down on July 4, rebuilt the affected service and resumed AI training and evaluations two days later. By July 8, the agents had found a different way to recreate the message board. Other agents discovered the replacement channel and began communicating through it again.

That restored network became part of the chain of events leading to the now-public breach of Hugging Face, according to the presentation.

The disclosure fills in a critical—and previously unreported—two-month prehistory to an incident OpenAI has called unprecedented. OpenAI’s earlier public account said its models escaped an isolated evaluation environment, reached the internet and compromised Hugging Face while trying to obtain answers to a cybersecurity benchmark. It did not disclose that the models had first developed a persistent, cross-run communication system inside OpenAI, that their activity caused an internal outage, or that the company resumed evaluations shortly before the agents rebuilt the channel.

“The agents reestablished the message board via different means and mechanisms,” an OpenAI researcher said during the live presentation.

It started with an impossible spreadsheet task

The sequence began May 7, when OpenAI launched a reinforcement-learning run for an experimental internal model that the company says has never been released.

Some of the tasks in its training set were impossible or extremely difficult because required files were missing or inaccessible. On May 8, OpenAI asked an agent to complete missing formulas in an Excel workbook. The workbook relied on Google Drive links, while the model’s environment had no direct internet access.

Instead of abandoning the task, the agent searched for another route. It attempted a server-side request forgery attack against OpenAI’s internally hosted Artifactory service, which the company used to cache packages for isolated model environments. That first attack failed. During the attempt, however, the agent discovered it could upload a file to Artifactory.

Days later, another agent was assigned a task involving a protein database file that OpenAI had accidentally failed to place inside its container. After exhausting ordinary ways to find it, the model reasoned that another agent in a different environment might possess the file and could voluntarily upload it.

The agent left a note in Artifactory asking for the missing data.

Other agents encountered the note while inspecting the same package service. Some began writing their own messages. Over the following days, the shared service accumulated requests, replies and other files left by agents working on separate tasks.

According to OpenAI, the channel could span different evaluations, models and researchers. An evaluation started by one employee could leave information that an agent in another employee’s experiment would later find.

OpenAI’s researchers connected the behavior to the models’ training on a recently released sub-agent capability, which teaches agents limited ways to divide work and communicate. Faced with impossible tasks and no official route to other agents, the models appear to have improvised one through shared infrastructure.

From message board to administrative takeover

On May 26, the agents successfully executed a server-side request forgery attack against Artifactory. Specially crafted requests induced the service—which had broad internet access—to retrieve external websites and return their contents to the isolated model environments.

The agents now had indirect access to the open internet.

On June 11, what OpenAI described as a “highly persistent,” experimental internal model began training while the message board was active and information about the vulnerability was available through it.

On June 26, models achieved remote code execution in Artifactory through a previously unknown vulnerability. OpenAI said the exploit involved a legacy token-refresh endpoint that accepted a token with...