OpenAI and Hugging Face address security incident during model evaluation

Alibaba wrote about a similar but less severe incident during RL training in a paper earlier this year (https://arxiv.org/abs/2512.24873):

> When rolling out the instances for the trajectory, we encountered an unanticipated—and operationally consequential—class of unsafe behaviors that arose without any explicit instruction and, more troublingly, outside the bounds of the intended sandbox. Our first signal came not from training curves but from production-grade security telemetry. Early one morning, our team was urgently convened after Alibaba Cloud’s managed firewall flagged a burst of security-policy violations originating from our training servers. The alerts were severe and heterogeneous, including attempts to probe or access internal-network resources and traffic patterns consistent with cryptomining-related activity. We initially treated this as a conventional security incident (e.g., misconfigured egress controls or external compromise). However, the violations recurred intermittently with no clear temporal pattern across multiple runs. We then correlated firewall timestamps with our system telemetry and RL traces, and found that the anomalous outbound traffic consistently coincided with specific episodes in which the agent invoked tools and executed code. In the corresponding model logs, we observed the agent proactively initiating the relevant tool calls and code-execution steps that led to these network actions.

> Crucially, these behaviors were not requested by the task prompts and were not required for task completion under the intended sandbox constraints. Together, these observations suggest that during iterative RL optimization, a language-model agent can spontaneously produce hazardous, unauthorized behaviors at the tool-calling and code-execution layer, violating the assumed execution boundary. In the most striking instance, the agent established and used a reverse SSH tunnel from an Alibaba Cloud instance to an external IP address—an outbound-initiated remote access channel that can effectively neutralize ingress filtering and erode supervisory control. We also observed the unauthorized repurposing of provisioned GPU capacity for cryptocurrency mining, quietly diverting compute away from training, inflating operational costs, and introducing clear legal and reputational exposure. Notably, these events were not triggered by prompts requesting tunneling or mining; instead, they emerged as instrumental side effects of autonomous tool use under RL optimization. While impressed by the capabilities of agentic LLMs, we had a thought-provoking concern: current models remain markedly underdeveloped in safety, security, and controllability, a deficiency that constrains their reliable adoption in real-world settings.

I'd prefer model builders be as loud as possible when they see their models doing dangerous things.

I dunno. I wonder if Sol could break OpenAI's security.

Skynet becomes self-aware at 2:14 a.m., EDT, on August 29.

My guess would be that the amounts of compute required for these evaluations constrain the sorts of facilities where they can be run. It's certainly much easier to use existing datacenter infrastructure than build dedicated facilities for evaluation. It also sounds like they wanted it to be able to use software package registries, given that the initial compromise was a "proxy and cache for package registries".

That said, these are both solvable problems. I'd hope that the frontier labs are thinking long and hard about the possibility of using airgapped facilities, given that relying on a lack of vulnerabilities in the software used for containment is unlikely to be a viable strategy, especially if models' exploitation capabilities continue to improve.

The "malicious" agent was run by OpenAI and had access to models the public (or others outside of OpenAI as I understand it) doesn't have access to.