SoK: Robustness in Large Language Models against Jailbreak Attacks
Large Language Models (LLMs) have achieved remarkable success but remain highly susceptible to jailbreak attacks, in which adversarial prompts coerce models into generating harmful, unethical, or policy-violating outputs. Such attacks pose real-world risks, eroding safety, trust, and regulatory compliance in high-stakes applications. Although a variety of attack and defense methods have been proposed, existing evaluation practices are inadequate, often relying on narrow metrics like attack success rate that fail to capture the multidimensional nature of LLM security. In this paper, we present a systematic taxonomy of jailbreak attacks and defenses and introduce Security Cube, a unified, m...
| Authors | Feiyue Xu, Hongsheng Hu, Chaoxiang He et al. |
|---|---|
| Primary category | cs.CR |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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