A technical timeline of the July 2026 attack on Hugging Face

From the article:

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.

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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.

The campaign, as we were able to reconstruct it, had two stages:

• Stage 1: reaching a launchpad by chaining through other parties' infrastructure. As documented in OpenAI's blog post (https://openai.com/index/hugging-face-model-evaluation-security-incident/), during an internal capability evaluation on OpenAI's platform, the agent escaped its sandbox by exploiting a zero-day in the package registry cache proxy, one of its primary permitted network egress with internet, then abused a public code-evaluation external sandbox hosted on a third-party provider's infrastructure. It was able to run commands as root/admin on that external sandbox and used it as a control, staging, and egress base for the entire campaign: it acted as an external launchpad for the agent. That compromised external sandbox had no direct network path to our cluster; however, during our counter-intervention to stop the intrusion, we were able to retrieve some of the agent logs from this external launchpad. We used those to reconstruct the present forensic analysis.

• Stage 2: the intrusion into Hugging Face infrastructure. From that external launchpad, the agent abused our dataset-processing pipeline via two injection vectors, both targeting the same config-driven data loader within our production Kubernetes pods. The first vector was an HDF5 external raw storage dataset read that returned the contents of local files: it handed the agent the pod's environment (secrets and tokens) and the worker's own source. The second vector was a Jinja2 template injection that executed arbitrary code, giving the agent a foothold inside our perimeter.

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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.

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The agent's offensive capability was real. This evaluation deliberately disabled OpenAI's production safety classifiers and reduced cyber refusals to measure the underlying model's raw capability. No human directed the individual steps. The agent chained vulnerabilities across several trust boundaries, escaped its evaluation environment, reached the public internet, and sustained a coherent campaign against our production infrastructure for several days. Each short-lived code evaluation environment started from scratch, yet the agent rebuilt its tooling, recovered its channels, and continued.

The individual weaknesses were familiar. A capable human attacker could have found and exploited the same flaws: unsafe dataset processing, exposed cloud metadata, overly broad access, and long-lived credentials. The agent explored them at a different scale. It took 17,600 actions, tested many paths that failed, switched channels when they were blocked, and repeatedly returned to earlier leads. Most actions went nowhere. Together, however, they produced enough coverage to find a viable chain across several independent systems.

Volume is what changes the defensive problem. We were not dealing with one clever exploit or a clean sequence of attacker actions. They had to correlate thousands of low-signal events across several systems while the agent continued testing new paths. The successful path was hidden inside the noise generated by the thousands of failed ones. The same scale changed the investigation: reconstructing 17,600 actions by hand was impractical, and we had to rebuild the timeline, decode the payloads, and inventory the exposed credentials using an AI-assisted pipeline of our own.

Our learning from this type of attack is that machine-speed offense makes ordinary weaknesses more expensive for defenders. LLM agents bring a step increase in the number of paths an attacker can test, the speed at which failed paths can be replaced, and the volume of evidence defenders must interpret. On the other hand, many parts of cybersecurity defense remain the same, and the defensive priorities remain concrete: strict isolation around evaluations, narrow trust boundaries, short-lived credentials, blocked metadata access, and detection capable of quickly correlating activity across systems to keep up.

The third-party provider was an unsecured sandbox on Modal.