ProSafePrune: Projected Safety Pruning for Mitigating Over-Refusal in LLMs

Large Language Models (LLMs) excel in various domains, but their safe deployment faces the challenge of balancing safety and utility. Existing alignment strategies often strengthen refusal mechanisms to reduce harmful outputs, but harmless instructions with superficial risky words are mistakenly rejected, which is known as over-refusal. This work first reveals that over-refusal stems from a cognitive bias in the model's internal representation space: LLMs naturally encode safety attributes in hidden states, and pseudo-harmful instructions overlap with harmful features, causing over-harmful encoding. To address this, we propose ProSafePrune, a subspace-projected low-rank parameter pruning framework for mitigating LLM over-refusal. By projecting pseudo-harmful features into subspaces and removing low-rank directions corresponding to harmful components in the most discriminative layers, we significantly reduce over-refusal while preserving the model’s ability to reject genuinely harmful requests, improving performance on general tasks. In experiments, across different models, our method significantly lowers the average false rejection rate while slightly improving general task performance.