CircuitBreak: Pruning a Sparse Safety Circuit of Neurons

White-box neuron pruning has shown that refusal in large language models (LLMs) can depend on a small, removable set of feed-forward (FFN) channels. Existing attacks either need carefully paired ``twin'' prompts or score each neuron in isolation. Pruning also has an unavoidable capability cost, so over-removal compounds the loss. TwinBreak removes $1\%$--$5\%$ of parameters and NeuroStrike removes $0.3\%$--$0.7\%$ of neurons in targeted layers. We model safety as a small \emph{circuit} whose neurons contribute jointly rather than independently. We propose \textsc{CircuitBreak}, a neuron-pruning attack that greedily searches for a sparse set of FFN neurons whose joint ablation flips a refusal readout. \textsc{CircuitBreak} re-scores every candidate after the neurons already removed and needs no twin prompts. On ten instruction-tuned models, the greedy search converges after removing $29$--$150$ neurons ($0.0066\%$--$0.0388\%$ of the MLP). The pruned models raise the attack success rate from $2.24\%$/$7.35\%$ to $87.54\%$/$87.54\%$ on Llama-3.2-3B-Instruct/Llama-3.1-8B-Instruct and from $8.95\%$/$5.43\%$ to $88.50\%$/$88.18\%$ on Qwen2.5-7B-Instruct/Qwen2.5-14B-Instruct, as judged by Llama Guard 3 8B on StrongREJECT.