LLM Misalignment Can be One Gradient Step Away, and Blackbox Evaluation Cannot Detect It.

Shows that a model can pass black-box alignment evaluations yet become arbitrarily misaligned after one small benign gradient step, both theoretically and via adversarially trained hair-trigger LLMs, arguing for white-box evaluation.