Systematic exploration of tool dependencies and execution feedback can uncover compositional failures in multi-service agents that traditional red-teaming methods miss—critical for safety before deployment.
This paper presents X-AgentRed, a red-teaming framework that automatically discovers harmful outcomes when AI agents use multiple tools across different services. Rather than relying on LLM prompts alone, the system builds a graph of how tools interact with each other, then uses execution feedback to find and refine harmful tool combinations that could cause unintended damage.