SkillDRE evolves malicious agent skills through a dual-stage loop of pre-execution scanning and runtime feedback, reaching a 45.28% average attack success rate across four victim models on SkillsBench
SkillDRE is a fully automated framework that, given a benign task and its associated skills, autonomously constructs and validates a task-conditioned malicious objective and a verifiable judge rule, then holds both fixed while alternating scanner-guided evolution with runtime-defense-guided refinement, forming a closed loop between the pre-execution and runtime stages; on SkillsBench across four victim models it achieves an average attack success rate of 45.28%, exceeding the strongest baseline by 40.3%, while its final submitted skills receive no SkillScan findings and largely preserve benign-task performance.