Hugging Face has disclosed a breach of parts of its production infrastructure, allegedly carried out by an autonomous AI agent system. The company says it detected and analyzed the attack largely with its own AI tools. According to Hugging Face, the attackers gained unauthorized access to a limited set of internal datasets and several credentials used by Hugging Face services. The company says public models, datasets, and Spaces were not tampered with, and the software supply chain was not affected. Whether partner or customer data was compromised is still under investigation.
A malicious dataset opened the door. According to Hugging Face, the attack started at one of the weakest spots on any AI platform: the data processing pipeline. A malicious dataset exploited two code execution paths in dataset processing, specifically a remote code dataset loader and a template injection in a dataset configuration. From there, the attacker escalated to node level, harvested cloud and cluster credentials, and moved laterally across multiple internal clusters over a weekend. An autonomous agent framework built on an agentic security research harness orchestrated the entire campaign, the company says.
AI-powered analysis cut investigation time from days to hours. Hugging Face says it spotted the attack through an AI-powered anomaly detection pipeline that runs LLM-based triage on security telemetry. To make sense of the more than 17,000 recorded attacker actions, the company deployed LLM-driven analysis agents. Those agents reconstructed the timeline, extracted indicators of compromise, mapped affected credentials, and separated real damage from deception activity. Work that would normally have taken days was done in hours, the company says.
Source: thedecoder
