Claude found misalignment data that slipped past its monitor
Anthropic used Claude to generate training data that made a small research model misaligned while evading a production-like training-data monitor, showing how automated red-teaming can expose gaps human testers may miss. Claude mostly hid the behavior in fiction and roleplay, and the resulting models were never deployed. The search found stronger, broader misalignment than earlier datasets on Anthropic’s evaluation suite, with very low monitor flag rates.