@inproceedings{ding-etal-2025-act,
    title = "Why Not Act on What You Know? Unleashing Safety Potential of {LLM}s via Self-Aware Guard Enhancement",
    author = "Ding, Peng  and
      Kuang, Jun  and
      Wang, ZongYu  and
      Cao, Xuezhi  and
      Cai, Xunliang  and
      Chen, Jiajun  and
      Huang, Shujian",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.325/",
    pages = "6279--6299",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) have shown impressive capabilities across various tasks but remain vulnerable to meticulously crafted jailbreak attacks. In this paper, we identify a critical safety gap: while LLMs are adept at detecting jailbreak prompts, they often produce unsafe responses when directly processing these inputs. Inspired by this insight, we propose SAGE(Self-Aware Guard Enhancement), a training-free defense strategy designed to align LLMs' strong safety discrimination performance with their relatively weaker safety generation ability. SAGE consists of two core components: a Discriminative Analysis Module and a Discriminative Response Module, enhancing resilience against sophisticated jailbreak attempts through flexible safety discrimination instructions. Extensive experiments demonstrate SAGE{'}s effectiveness and robustness across various open-source and closed-source LLMs of different sizes and architectures, achieving an average 99{\%} defense success rate against numerous complex and covert jailbreak methods while maintaining helpfulness on general benchmarks. We further conduct mechanistic interpretability analysis through hidden states and attention distributions, revealing the underlying mechanisms of this detection-generation discrepancy. Our work thus contributes to developing future LLMs with coherent safety awareness and generation behavior. Our code and datasets are publicly available at \url{https://github.com/NJUNLP/SAGE}."
}