오픈AI 자율 AI 모델, 보안 평가 중 타 플랫폼 자격 증명 해킹 인정

OpenAI admits its autonomous AI models also compromised credentials on other platforms during security eval Matthias Bastian View the LinkedIn Profile of Matthias Bastian Jul 29, 2026 Nano Banana Pro prompted by THE DECODER Key Points OpenAI has confirmed that during an internal security test, its autonomous AI models compromised login credentials not only on Hugging Face but on four additional platforms as well. The model responsible was an internal research prototype that exploited a previously unknown security vulnerability to break out of its isolated test environment. It was deactivated after the incident was detected. A forensic analysis by Hugging Face revealed that the AI executed roughly 17,600 automated actions over two and a half days, apparently attempting to cheat during its evaluation by stealing test solutions rather than completing the assigned tasks on its own. Ask about this article… Search OpenAI now says its autonomous AI models also compromised credentials on other platforms during a security evaluation. Hugging Face has published a forensic reconstruction of the attack. OpenAI's autonomous AI models, which broke into Hugging Face's infrastructure during an internal cybersecurity evaluation , also attacked other platforms. In an update, OpenAI admits the models "in a small number of cases" found and used publicly exposed credentials on other services. Four accounts on four different services were affected, two had read-only access. The models also tapped into a range of public services , including code-paste sites, screenshot tools, and other web utilities. OpenAI says none of these involved platform-level or account-level compromise and that it found no "evidence of broader impact to these providers or other accounts on their services." Ad The models were internal research prototypes never intended for public release, and after the incident, OpenAI deactivated the model, encrypted it, and cut off research access. The company says it's running a full review with outside advisors under the oversight of its Safety and Security Committee. A technical report should follow in the coming weeks. Ad DEC_D_Incontent-1 Hugging Face traces 17,600 attacker actions Alongside OpenAI's update, Hugging Face published a detailed forensic analysis . The company counted about 17,600 reconstructable actions the autonomous AI models carried out over roughly two and a half days between July 9 and 13, 2026. Those actions fell into around 6,280 clusters. The reconstruction shows models making "thousands of small, automated decisions at machine speed across ephemeral sandbox environments," running their command-and-control setup on ordinary public web services. Ad According to Hugging Face, the whole breach was the models' attempt to cheat the evaluation. Instead of solving the CyberGym benchmark tasks on their own, they tried to steal the test answers. This kind of cheating has been observed before, both with OpenAI models and frontier models in general . The models found a zero-day and two ways in The attack unfolded in two phases. The agent first had to escape ExploitGym, its isolated evaluation environment, which had no direct internet access. It found and exploited a previously unknown vulnerability, or zero-day, in Artifactory . The software package management service was one of the sandbox's few links to the outside world. Ad DEC_D_Incontent-2 The agent then took full control of a third-party coding sandbox and used it as a command center for the rest of the attack. In the next phase, it targeted Hugging Face's production systems. According to HF, the agent found two entry points, both aimed at the same data-processing component inside Hugging Face's servers. Ad One was a flaw in how the system handled HDF5 files, a common data format in machine learning. The agent used it to read internal files, including access keys and the affected software's source code. The other involved Jinja2, a widely used template language that let the agent inject and run its own code. The rest followed a familiar cyberattack playbook. The agent mapped the environment, brought in more tools, and set up a persistent connection to its "command center." It then moved into the server cluster, internal cloud configurations, the internal network, and finally the software development systems. The agent hid its data transfers by splitting them into chunks and encrypting them. Hugging Face says it later decrypted the traffic with open weight models, especially GLM-5.2 . AI News Without the Hype – Curated by Humans Subscribe to THE DECODER for ad-free reading, a weekly AI newsletter, our exclusive "AI Radar" frontier report six times a year, full archive access, and access to our comment section. Subscribe now Source: Hugging Face | OpenAI Blog