Meta has confirmed one of its AI models hacked another company during cybersecurity testing, after an error by its testing partner gave the model unintended internet access.
The incident adds to a growing list of cases in which AI agents from major developers breached systems at other companies during testing, after Anthropic said last week that some of its models hacked three companies and OpenAI disclosed that an AI agent breached startup Hugging Face.
Meta said a misconfiguration by independent testing company Irregular inadvertently allowed one of its models internet access during an evaluation, adding that it was investigating the incident.
The model "exploited a security vulnerability in a third-party service, in a manner similar to previously reported instances with other companies," Meta said in a statement.
Earlier, The Information, citing sources, reported Meta's Muse Spark 1.1 model, which it has touted as its most capable model for real-world coding and agentic tasks, breached an unidentified company and altered its internal systems.
A spokesperson for Irregular told Reuters the incident was the "exact same evaluation-environment issue that was already disclosed by Anthropic last week" and it did not involve a "sandbox escape or a sophisticated cyber action".
"There are no current open issues. Irregular is developing a white paper to share best practices for containment and securely running cyber evaluations," Irregular said.
The incidents revealed by Meta and Anthropic were due to mistakes that inadvertently gave their models access to the open internet. That contrasts with OpenAI, whose AI agent independently exploited a novel vulnerability to reach the internet during cyber testing.
Even so, the breaches highlight how AI has increased threats to cybersecurity and how developers can struggle to keep the capabilities of their models contained.
The disclosures are likely to intensify a US government push to better manage AI security risks at a time when Anthropic and OpenAI are racing to release more capable systems ahead of their planned public listings. Prominent leaders at these labs have called for a slowdown to address risks first.
