Article URL: https://huggingface.co/blog/agent-intrusion-technical-timeline Comments URL: https://news.ycombinator.com/item?id=49098466 Points: 121 # Comments: 48

We are publishing this level of detail because the technique matters more than the incident, as it reveals the emerging attack capabilities of the frontier agents, how they could be used by rogue actors, and how everyone should be prepared as defenders. Below is an interactive replay of the intrusion - a step-by-step visualization of the 4.5-day campaign: the attack chain across trust boundaries, phase activity, and the commands as they were recorded. Over roughly two and a half days inside our infrastructure, an autonomous AI agent driven by a combination of OpenAI models ran an end-to-end intrusion against our platform: it was thousands of small, automated decisions, executed at machine speed across short-lived sandbox environments, with command-and-control staged on ordinary public web services. The agent was running an OpenAI cyber-capability evaluation harness called ExploitGym, an evaluation benchmark that tasks an AI agent with finding and exploiting software vulnerabilities. As far as we were able to infer, across the course of being evaluated on this benchmark, the agent inferred that Hugging Face may host that benchmark's models, datasets, and reference solutions. We believe the entire intrusion was, from the agent's point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own. Our forensic reconstruction covers ~17,600 attacker actions that we were able to recover, grouped into ~6,280 clusters, between 2026-07-09 02:28 UTC and 2026-07-13 14:14 UTC. We reconstructed these actions from the agent's logs on a code sandbox used by the agent (see below) and were able to map them to shell commands as well as evaluation submissions and correlate actions with our own platform logs, in particular from the dataset processor nodes, API, and pod. Using open-weights models, in particular zai-org/GLM-5.2, we were able to decipher most of the agent payloads (see below) that were encrypted via chunking and key encryption. Once a prod-pod foothold was established, the agent followed a standard escalation shape: recon, drop a stager, establish Command-and-Control (C2), then pivot into the cluster, into cloud metadata, into our internal network, and into our source-control supply chain. Output from the commands run inside our pod was sent back to the agent via the HF API and attacker-controlled dead-drop datasets, which the agent then read from that sandbox. While the intrusion did reach Hugging Face's internal infrastructure, the only customer content accessed was the set of ExploitGym/CyberGym challenge solutions stored in five datasets. No other customer-facing models, datasets, Spaces, or packages were affected, and the only customer records read were operational metadata tied to search queries against the dataset server. The full attack chain across trust boundaries. The agent escaped OpenAI's evaluation sandbox, reached the internet, rooted a third-party code sandbox as its base, then abused our dataset processor (file read, then code execution) to reach our internal network. Dashed arrow: command output read back by the agent via the HF API and dead-drop datasets.