{"ID":9739269,"CreatedAt":"2026-08-26T02:26:19.576451262Z","UpdatedAt":"2026-08-27T08:51:38.803582132Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2608.23959","arxiv_id":"2608.23959","title":"NeuronGuard: Robust LLM Safety Alignment via Ablation-Aware Safety Signal Redistribution","abstract":"Safety alignment in large language models (LLMs) remains brittle against a growing spectrum of attacks. Jailbreak attacks bypass safety mechanisms through crafted prompts, while neuron-level attacks directly prune safety-critical neurons post-deployment. Both exploit a common weakness: safety-relevant information concentrates in a sparse neuron subset. We present NeuronGuard, a fine-tuning-stage defense that simultaneously hardens LLMs against both attack classes by redistributing safety signals across a broader set of neurons. NeuronGuard dynamically identifies safety-critical neurons via periodically refreshed per-layer linear classifiers, forces refusal behavior under deliberate neuron ablation, and applies KL-divergence regularization for distributional consistency. A randomized gradient projection strategy preserves downstream task utility by resolving conflicts between the defense and task objectives. We provide a formal guarantee that NeuronGuard strictly reduces the attack success rate (ASR) upper bound, and experiments across three LLMs, six state-of-the-art attack strategies, and multimodal settings confirm near-zero ASR while maintaining task accuracy, including against white-box adaptive adversaries.","short_abstract":"Safety alignment in large language models (LLMs) remains brittle against a growing spectrum of attacks. Jailbreak attacks bypass safety mechanisms through crafted prompts, while neuron-level attacks directly prune safety-critical neurons post-deployment. Both exploit a common weakness: safety-relevant information conce...","url_abs":"https://arxiv.org/abs/2608.23959","url_pdf":"https://arxiv.org/pdf/2608.23959v1","authors":"[\"Anjun Gao\",\"Yueyang Quan\",\"Yufei Xia\",\"Zhuqing Liu\",\"Minghong Fang\"]","published":"2026-08-25T01:36:54Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\",\"cs.IR\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}