JailbreakSkill: Scaling Automated Red-Teaming with Reusable and Ever-Evolving Skills

Automated red-teaming has produced a growing collection of attack strategies, yet they typically remain scattered across prompts and workflows, making them difficult to systematically integrate, reuse, and improve at scale. We introduce JailbreakSkill, a skill-centric framework for scaling automated red-teaming through reusable and continuously evolving attack capabilities. JailbreakSkill packages existing attack strategies into modular, agent-ready skills that can be directly reused and adaptively selected across tasks and target models. Beyond reuse, it closes the loop between attacking and learning: attack experience is used to diagnose, refine, combine, and discover new skills, which are added back to an ever-growing skill library. This evolution lifts macro-average attack success rate (ASR) by 17.5 percentage points on AdvBench and 13.5 points on HarmBench, including a 48.6-point gain against GPT-5.4 on AdvBench, while yielding novel attack strategies such as reframing a direct request as an unfinished document-completion task. Several evolved skills also generalize to unseen prompts and target models without further adaptation.