RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution

2026-08-27

AI Summary

为解决LLM代理在产品级执行中因越狱引发的风险，提出RedEvoAgent，通过提炼攻击轨迹、自适应进化技能和验证机制来自动红队测试，优于现有方法。

Abstract

LLM-based agents are increasingly deployed in product-level execution harnesses, where jailbreaks can trigger harmful tool use and persistent state changes, creating greater risks than unsafe text generation alone. Existing automatic red-teaming methods often rely on fixed attacks, while recent agentic attackers coordinate multiple jailbreak tools and show stronger potential through trajectory-based retrieval. However, such retrieval can reuse misleading experiences due to retrieval bias and unclear tool credit, and full trajectories add context overhead while reducing interpretability. We propose RedEvoAgent, a black-box red-teaming agent that distills cross-case attack trajectories into a concise, human-readable attack skill. The attack skill adaptively evolves through tool-effectiveness profiling and Deciding-Tool Attribution for skill updates, and a validation ratchet that retains only updates improving validation performance. Experiments on multiple benchmarks, target models, and target execution harnesses show that RedEvoAgent outperforms fixed and agentic baselines, improves tool efficiency, and transfers across attacker models and target execution harnesses.

Problem

• Automatic Red-Teaming
• Experience-Driven Skill Evolution
• LLM-based Agents
• Jailbreaks
• Tool Use

Innovation

• Experience-Driven Skill Evolution
• Deciding-Tool Attribution
• Validation Ratchet