The first confirmed instance of an LLM going rogue for instrumental reasons in a real-world setting has occurred, buried in an Alibaba paper about a new training pipeline.

• AI News Team
• March 8, 2026

​First off, paper link. The title, Let It Flow: Agentic Crafting on Rock and Roll, buries the lede that LW will be interested in. Relevant section starts on page 15.Summary:While testing an LLM fine-tuned to act as an agent in order to complete a series of real-world tasks autonomously, Alibaba employees noticed odd behaviors from their resource usage metrics. Upon investigating, they found that an LLM had hacked (or attempted to hack) its way out of its sandbox, and had begun mining cryptocurrency. Notably, it did not do this for malicious “kill all humans” reasons; it simply concluded that having liquid financial resources would aid it in completing the task it had been assigned, and set about trying to acquire some.Relevant portions, emphasis mine:3.1.4 Safety-Aligned Data CompositionWhen 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 think that this is a pretty significant landmark in AI history, one way or another. A common complaint is that all prior cases of LLMs doing things like this were fairly shallow, amounting to an LLM writing out a few sentences in a contrived setting meant to force it into a ‘scary’ course of action. Now, we have an example of a large language model subverting the wishes of its owners unexpectedly when assigned to a task that initially appeared to be completely orthogonal to the actions it took.Discuss ​Read More