UK AISI releases methodology to test whether AI systems will misbehave — a new way to spot alignment risks before they cause harm

UK AISI published a paper outlining how to measure what large language models (AI systems that understand and generate text) will actually try to do, with a focus on identifying misaligned behavior — actions that don't match human intentions — rather than just testing what they're capable of.

The methodology distinguishes between two types of AI safety research: theoretical work proving whether misalignment *can* happen, versus practical testing that predicts whether a specific AI *will* try to misbehave in real deployments. The paper prioritizes the latter by proposing ways to model how AI systems make decisions.

For AI safety teams and companies deploying large language models, this gives them a framework to catch potentially dangerous tendencies before release — similar to how Anthropic tests for unintended agent behavior — reducing the risk that an AI system optimizes for the wrong goals in production environments.

The paper is available as a methodology guide independent of technical appendices, making it accessible to safety teams building evaluation procedures for their own AI systems.