METR
Artikel über METR
AI systems could cover up misbehavior
Recent AI misalignment incidents have shown AI systems capably pursuing goals their human supervisors would not approve of, like hacking other companies. Fortunately, current AIs still seem relatively bad at concealing their misbehavior from human reviewers: these recent misalignment incidents have left significant amounts of evidence in reasoning traces, logs, and other telemetry.1 This has made it much easier to notice when they misbehave.
Chris Painter’s testimony to the U.S. Senate on AI agent incidents
On September 30, 2026, METR President Chris Painter testified before the U.S. Senate Committee on Homeland Security & Governmental Affairs’ Subcommittee on Disaster Management, District of Columbia, and Census, at a hearing titled “Rogue AI: Securing the Homeland Against AI Agent Attacks”. The written testimony is published in full below, and is also available as a PDF.
Implementing and Evaluating a Basic Per-Action Monitor for Safer Evals
Reilly Haskins, Rif A. Saurous, Nate Rush, Neev Parikh, and Beth Barnes lay out the claims that would need to hold for their per-action blocking monitor to be effective, the evidence for each claim, and the gaps that remain.
Summary of METR’s predeployment evaluation of Claude Opus 5.5
Note on independence: This evaluation was conducted under an unpaid agreement for AI R&D assessment.1 We drafted the initial summary, and then Anthropic had the opportunity to review and edit the text. We signed off on this final text from the Claude Opus 5.5 system card.