Evolving Skill-Structured Attack Memory Enhances LLM Jailbreaking

Abstract

Jailbreak attacks on large language models (LLMs) aim to induce LLMs to produce content that they are expected to refuse. Automated black-box jailbreak generation is especially important for safety evaluation, where the attacker observes only model outputs and needs to automatically search for effective adversarial prompts. Existing black-box jailbreak methods either depend on sample-wise heuristic search or leverage attack experience through accumulating strategy pools or method libraries, lack

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@misc{zhang2026evolving,
  title = {{Evolving Skill-Structured Attack Memory Enhances LLM Jailbreaking}},
  author = {Junke Zhang and Jianwei Wang and Sishuo Chen and Yizhang He and Qingshuai Feng and Zhengyi Yang},
  year = {2026},
  month = may,
  eprint = {2605.29237},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.29237}
}