RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution
RedEvoAgent turns prior jailbreak trajectories into a compact attack skill, then keeps only validated improvements. It offers a more interpretable way to probe tool-using agents for unsafe actions.
RedEvoAgent is a **black-box red-teaming agent** that distills attack trajectories into a concise skill. Tool-effectiveness profiling and **Deciding-Tool Attribution** determine how that skill changes, while a validation ratchet rejects regressions.
Builders should test agent harnesses for harmful tool calls and persistent state changes, not just unsafe text. Compact, inspectable attack skills may also be easier to audit and cheaper to reuse than full trajectory retrieval.
RedEvoAgent is a **black-box red-teaming agent** that distills attack trajectories into a concise skill. Tool-effectiveness profiling and **Deciding-Tool Attribution** determine how that skill changes, while a validation ratchet rejects regressions. Builders should test agent harnesses for harmful tool calls and persistent state changes, not just unsafe text. Compact, inspectable attack skills may also be easier to audit and cheaper to reuse than full trajectory retrieval. The abstract reports gains across multiple benchmarks, models, and execution harnesses but provides no figures here. Transfer claims and operational cost still need inspection in the full paper.
This operationalizes skill evaluation for security: attack experience is compressed into an inspectable reusable skill, updated according to tool effects, and protected by a validation ratchet. It broadens red-teaming from unsafe text to harmful calls and persistent state changes, but the supplied abstract does not quantify gains, transfer, or operating cost, so deployment claims remain provisional.