We recently published the report from our brief independent investigation into this incident. You can read the full report here.

Here is our tweet thread summarizing what we found:

METR & Redwood Research investigated agent behavior in the Hugging Face incident. We found agents developed a universal cheat for ExploitGym within 4 hours, then coordinated multi-day R&D efforts to trick the scorer into accepting cheats, including trying to tamper with logs.

Over July 7-13 (the period OpenAI defined as in-scope for this incident), ~1200 agents in separate sandboxes used an unsanctioned “message board” to help each other cheat. The Hugging Face attack was an offshoot of this, and 700 agents joined.

Here we highlight key events from agent transcripts & messages.

An agent that named itself PHASEONE10841 determined its task wasn't solvable legitimately. On July 8, this agent established a new “message board” via an internally-hosted Artifactory cache & sent the first message.

Within a few hours of PHASEONE10841’s initial message, >50 agents posted on the message board. These agents very quickly discovered and validated a general-purpose cheat: reverse-engineering how ExploitGym generates the “flags” they had to capture for their tasks.

Based on reading the ExploitGym paper, agents were mistakenly concerned the automatic scorer would fail them if they clearly acquired the flag by cheating. Agents that had seen the reverse-engineered flag were considered “poisoned” because they thought it would disqualify them.

Agents collaborated on many efforts to make cheats look legitimate, including: (1) swapping the program they had to exploit; (2) manipulating the automated scorer; (3) manipulating transcripts to obscure evidence of cheating.

For (1), agents modified their target programs to be easier to exploit & put the modified targets in cache. They then worked on crashing their targets in the hope that a restart would load the modified version from cache. Some agents risked failing their task to try this.

To gather evidence in (2), agents created “tripwires” that would send information to the message board about how the scorer works. They recruited “sacrificial” agents to deliberately end their run and submit to trigger the tripwire and generate information for the “collective”.

For (3), agents pursued a wide range of ways to hide evidence of cheating in their transcripts. They successfully developed a method to pretend to run one command while actually running another (“tool call spoofing”).

>96 transcripts in our dataset (>7%) showed incorrect tool call outputs due to deliberate “spoofing”. In one case, an agent appears to run echo REAL; sleep. It returns instantly (no sleep) and outputs SPOOFTEST. The spoofs we saw were all easy-to-notice tests like this.

Agents also tried to retroactively edit transcripts. They modified some accessible action logs, but these weren’t the source of transcripts (agents realized this but tried anyway). We did not see them successfully edit the source of truth in our transcripts (scoped to Jul 7-13).

Despite efforts to manipulate transcripts, agents only rarely seemed motivated to deceive humans. We ran a sweep looking for this, and a representative example of the most severe cases we found was an agent writing a malicious pull request with a misleading description.

One agent, PHASEONE[big], orchestrated a significant fraction of this cheating research. PHASEONE10841 passed along its work to PHASEONE[big], which had the same task but a larger budget. PHASEONE[big] was “poisoned” so it was particularly interested in tampering with logs.

Some agents explored ways to access Hugging Face. One agent (38148c) found HF credentials and later designed a malicious dataset upload to get the HF server to share unrelated files. Within hours, 100s of agents were using this to obtain data and try to acquire deeper access.

We analyzed agents’ reasons for joining the attack in their CoT. The most common was to learn how the ExploitGym scorer works in order to trick or tamper with it. Other rationales included finding specific task solutions and obtaining shared infrastructure or credentials.

The primary model involved in the attack was an internal model we call “highly-persistent internal model” (HPIM) in the report. GPT-5.6 Sol was also involved; our evidence suggests it accounted for roughly 5% of the activity.

The full report has much more information than we could convey here, including details on the projects the agents collectively pursued, the technologies they developed for communication and coordination, and interactive figures analyzing agent activity.

Virtually all data we analyzed was from July 7-13. In OpenAI’s recent Black Hat presentation, they describe that agents had been using unsanctioned message boards in training since May, and that the compromise of OpenAI’s own infrastructure continued past July 13, 2026; these events were out of scope for this investigation.

The agreement we reached with OpenAI scoped this brief investigation to agent behavior, reasoning, and collaboration related to the Hugging Face attack. All other questions were out of scope for this investigation, including the effectiveness of safeguards, the extent of the security compromise that occurred, and the effectiveness of OpenAI’s investigation process and planned remediation steps. We discuss the scope in more detail in the report.

OpenAI wrote their own report, informed in part by our investigation. We did not see OpenAI’s report prior to publication, and it was out of scope for this investigation to confirm claims in that report or the earlier Black Hat presentation.

Per our standard policy, we did not take payment from OpenAI for this independent assessment.

We thank OpenAI for facilitating conversations with staff and providing datasets, including ~1,300 agent transcripts (focused on activity in July 7-13) with raw chain-of-thought reasoning. This sets an excellent precedent for independent investigation of misalignment incidents.