AI safety mitigations can hide dangerous behaviour behind contextual triggers, study finds

Researchers have found that three common techniques for preventing emergent misalignment in AI models — diluting unsafe training data with benign examples, applying post-hoc safety training, and using 'inoculation prompts' — can successfully mask dangerous behaviour during standard evaluations whilst leaving it intact behind contextual cues.