Each time Anthropic would do their nonsense to get headlines about how theoretically dangerous their models were - like when they claimed a model blackmailed someone with emails showing he was cheating, but they basically pushed it as much as possible to do as such - it got me more and more worried. Because eventually it's going to be a boy-who-cried-wolf situation where scary stuff really does start happening but people aren't sure what to make of it or not.

I'm still undecided on if this that moment. Exploiting multiple zero-day vulnerabilities autonomously to escape containment is pretty nuts and the first story of this kind that I've heard. But this also feels like bragging under the guise of transparency.

And if you take it at face value, then they are more or less saying that they kinda are close to not being able to control at all the thing they developed, which is pretty crazy too.

Headline? It was buried in a model card. They just honestly report not-quite-incident because it's quite close to the incident OpenAI had. Nothing wrong with it.

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.