ASPIRE: Agentic Safety and Prompt Injection Red-teaming Engine

LLM agents retrieve untrusted content and act through tools, creating indirect prompt-injection risks that can cause unauthorized actions or persistent state changes. Existing automated red-teaming largely optimizes payloads for pre-specified scenarios, leaving latent vulnerabilities across the agent's behavior space unexplored. We present ASPRIE, an Agentic Safety & Prompt Injection Red-teaming Engine for open-ended, behavior-level vulnerability discovery. ASPIRE maintains an evolving Agent Security Behavior Graph and uses complementary Explore and Exploit experts to discover, verify, and generalize consequence-centric tests. Trajectory evidence updates the graph and diagnoses partial or failed attempts, while cross-run memory transfers useful search strategies. Experiments on AgentDojo and AgentDyn show that ASPIRE substantially expands coverage across consequences, injection methods, environments, and behavior paths while maintaining strong attack success.