@inproceedings{zhao-etal-2026-unraveling,
    title = "Unraveling {LLM} Jailbreaks Through Safety Knowledge Neurons",
    author = "Zhao, Chongwen  and
      Ke, Yutong  and
      Huang, Kaizhu",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.83/",
    pages = "1889--1906",
    ISBN = "979-8-89176-380-7",
    abstract = "Large Language Models (LLMs) have achieved substantial progress in alignment, ensuring safer and more reliable outputs. However, jailbreak attacks can still bypass these safeguards and provoke harmful responses from well-aligned models. While some studies have achieved defenses against jailbreak attacks by modifying output distributions or detecting harmful content, the exact rationale still remains elusive. In this work, we present a novel neuron-level interpretability method that focuses on the role of safety-related knowledge neurons. Unlike existing approaches, our method projects the model{'}s internal representation into a more consistent and interpretable vocabulary space. We then show that adjusting the activation of safety-related neurons can effectively control the model{'}s behavior with a mean ASR higher than 97{\%}. Building on this insight, we propose SafeTuning, a fine-tuning strategy that reinforces safety-critical neurons to improve model robustness against jailbreaks. SafeTuning consistently reduces attack success rates across multiple LLMs and outperforms all four baseline defenses. These findings offer a new perspective on understanding and defending against jailbreak attacks."
}