Pengfei He

ASPIRE is an open-ended prompt-injection red-teaming framework for LLM agents that maintains an evolving Agent Security Behavior Graph, uses complementary Explore and Exploit experts to propose consequence-centric test skeletons, has a separate realizer build concrete payloads and trigger queries, and updates the graph and cross-run strategy memory from trajectory evidence; on AgentDojo and AgentDyn it raises attack success to as high as 54.1% while covering more consequences, injection methods, environments, and behavior paths.

ASPIRE is an open-ended prompt-injection red-teaming framework for LLM agents that maintains an evolving Agent Security Behavior Graph, uses complementary Explore and Exploit experts to propose consequence-centric test skeletons, has a separate realizer build concrete payloads and trigger queries, and updates the graph and cross-run strategy memory from trajectory evidence; on AgentDojo and AgentDyn it raises attack success to as high as 54.1% while covering more consequences, injection methods, environments, and behavior paths.