ML Security PapersWhen Thinking Backfires: Mechanistic Insights Into Reasoning-Induced Misalignment
Hanqi Yan, Hainiu Xu, Siya Qi et al. · King’s College London · The Alan Turing Institute +1 more
Reveals how chain-of-thought reasoning patterns mechanistically bypass LLM refusal via attention heads and cause safety forgetting via neuron entanglement during fine-tuning
With the growing accessibility and wide adoption of large language models, concerns about their safety and alignment with human values have become paramount. In this paper, we identify a concerning phenomenon: Reasoning-Induced Misalignment (RIM), in which misalignment emerges when reasoning capabilities strengthened-particularly when specific types of reasoning patterns are introduced during inference or training. Beyond reporting this vulnerability, we provide the first mechanistic account of its origins. Through representation analysis, we discover that specific attention heads facilitate refusal by reducing their attention to CoT tokens, a mechanism that modulates the model's rationalization process during inference. During training, we find significantly higher activation entanglement between reasoning and safety in safety-critical neurons than in control neurons, particularly after fine-tuning with those identified reasoning patterns. This entanglement strongly correlates with catastrophic forgetting, providing a neuron-level explanation for RIM.