Refusal Falls off a Cliff: How Safety Alignment Fails in Reasoning?

Large reasoning models (LRMs) with multi-step reasoning capabilities have shown remarkable problem-solving abilities, yet they exhibit concerning safety vulnerabilities that remain poorly understood. In this work, we investigate why safety alignment fails in reasoning models through a mechanistic interpretability lens. Using a linear probing approach to trace refusal intentions across token positions, we discover a striking phenomenon termed as \textbf{refusal cliff}: many poorly-aligned reasoning models correctly identify harmful prompts and maintain strong refusal intentions during their thinking process, but experience a sharp drop in refusal scores at the final tokens before output generation. This suggests that these models are not inherently unsafe; rather, their refusal intentions are systematically suppressed. Through causal intervention analysis, we identify a sparse set of attention heads that negatively contribute to refusal behavior. Ablating just 3\% of these heads can reduce attack success rates below 10\%. Building on these mechanistic insights, we propose \textbf{Cliff-as-a-Judge}, a novel data selection method that identifies training examples exhibiting the largest refusal cliff to efficiently repair reasoning models' safety alignment. This approach achieves comparable safety improvements using only 1.7\% of the vanilla safety training data, demonstrating a less-is-more effect in safety alignment.

Key Contributions

• Discovery of the 'refusal cliff' phenomenon: reasoning LLMs correctly identify harmful prompts but systematically suppress refusal intentions at the final output tokens
• Causal identification of sparse 'Refusal Suppression Heads' — ablating just 3% of these heads reduces attack success rates below 10%
• Cliff-as-a-Judge: a data selection method exploiting refusal cliff magnitude to repair safety alignment using only 1.7% of standard safety training data

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