Defenses against jailbreak attacks on Large Language Models (LLMs) operate at different pipeline stages, such as input modification or output guard, but it remains unclear which defenses to deploy at each stage and how to combine them. Prior empirical studies, fragmented by inconsistent attack-success-rate definitions...
AlcaTRAz (Anchored Tree-Rule defense Against jailbreaks), a prompt-level defense based on rule trees that operates exclusively on the input text and requires no modification or retraining of the target model, is proposed.
J. Res, Petr Kaska, Martin Perešíni et al.· 0 citations
A Systematization of Knowledge (SoK) that reframes jailbreak security around the full agentic execution pipeline, and develops unified taxonomies of attacks and defenses spanning user interaction, planning and reasoning, memory, tool use, and inter-agent communication, introduces a security--utility--efficiency evaluat...
Md. Jueal Mia, Yanzhao Wu, S. Uluagac et al.· 0 citations
Two universal tool-based defenses are introduced: Attacker Tool Filtering, which uses anomaly detection to identify and remove suspicious tools, and Normal Tool Recalling, a white-box method that restores the agent's original toolset prior to planning.
A mechanistic analysis of the jailbreaking behavior in a large-scale, safety-aligned LLM, focusing on LLaMA-2-7B-chat-hf identifies computational circuits responsible for generating affirmative responses to jailbreak prompts and uncovers key attention heads and MLP pathways that mediate adversarial prompt exploitation.
Paria Mehrbod, Boris Knyazev, Guy Wolf et al.· 0 citations
This work proposes a self-evolving test-time defense built around a persistent, cross-interaction rule memory that substantially reduces attack success rates while preserving benign utility, remains robust under an adaptive composite-wrapper attack, and does not increase over-refusal as the memory grows.
Tongshen Hu, Bryan Hooi· 0 citations
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