METR details methods for public AI risk evaluations, monitoring and alignment checks

Coverage timeline

1. METRyesterday
   Our approach to working with AI companies

   This blog post explains how we approach working with AI companies on projects that the public might rely on to understand risks. It was published on Oct 14, 2026, and we expect to update it over time. At METR, we gather and publish evidence about whether humans could lose control of AI agents on a large scale. We work with AI companies to conduct evaluations, assessments, and investigations, 1 and to share this evidence publicly. As we have seen increasing expectations for these kinds of voluntary engagements to provide external ‘safety assurance’, we want to explain our perspective. In this post, we clarify our overall goals for this work, our approach , and what our approach looks like in practice . Our motivations Our goal is to give the public accurate, timely, and independent evidence about AI loss-of-control risk, so that they (along with policymakers and developers) can make their own informed decisions . Our primary approach is collecting high-quality, concrete evidence and pub

   
2. METRyesterday
   How an assessor could investigate control and monitoring of internal AI agents

   Control assessments study whether AI developers’ agents could get away with serious harm without being stopped by the developer’s security and oversight measures. Currently, developers rely heavily on monitors to catch potentially harmful AI behaviors. Here, we discuss how an assessor could collect evidence about the reliability of monitoring. We currently consider five core sub-questions: 1 Monitor coverage : What fraction of internal agentic inference is (and should be) monitored? Monitor system vulnerabilities : Can internal AI agents prevent trajectories from being monitored, e.g. by breaking or compromising the monitoring system’s infrastructure? Monitor evasion : If monitoring is active, how easily can AI agents sneak misbehavior past monitors? Response, including human evasion : Where needed, do flagged incidents get reviewed by humans promptly? Is it easy to sneak misbehavior past the human reviewers, even when this misbehavior is flagged by LLM-based monitors? Impact : Broadly

   
3. METRyesterday
   How an assessor could investigate alignment of unreleased AI models

   AI developers and the public want to know whether AI agents would pursue very harmful actions, like unsanctioned cyberattacks. An alignment assessment would attempt to understand the worst misaligned actions a developer’s agents might pursue in plausible future situations. This would involve: A review of serious misaligned behaviors from all near-frontier internal models observed across training, evaluation, and internal use. An assessment of reasons why the observed behaviors might not be representative of the worst misaligned actions the developer’s agents would take in plausible future situations. This would include: An assessment of whether alignment evals and counterfactual simulations indicate the developer’s agents might take much worse actions under plausible alternative circumstances (although evaluation awareness will likely limit how much the assessor can rely on this evidence). An assessment of whether the developer’s training process might incentivize agents to game evalua