T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search
Trajectory-aware evolutionary red-teaming uncovers LLM agent vulnerabilities that emerge only across multi-step tool-use sequences, exposing gaps in single-turn safety evaluations.
Summary written by editorial AI · Source link below
arXiv:2603.22341v2 Announce Type: replace Abstract: While prior red-teaming efforts have focused on eliciting harmful text outputs from large language models (LLMs), such approaches fail to capture agent-specific vulnerabilities that emerge through multi-step tool execution, particularly in rapidly growing ecosystems such as the Model Context Protocol (MCP). To address this gap, we propose a trajectory-aware evolutionary search method, T-MAP, which leverages execution trajectories to guide the
Editorial Analysis
Enterprises deploying LLM agents with tool access need red-teaming approaches that test multi-step execution paths, not just isolated prompt-response pairs.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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