LLM Agent Security

The field examines how agents can be manipulated through indirect prompt injection, malicious tools or skills, poisoned memory, excessive permissions, and multi-agent interactions, sometimes leading to data leaks or harmful actions. Researchers evaluate these risks with red-team attacks and benchmarks that track agent trajectories, tool calls, and real effects—not just final answers. Defenses include runtime authorization and least privilege, sandboxing, provenance and information-flow controls, monitoring, and security-focused training. A central challenge is protecting agents over long, changing workflows without blocking legitimate tasks, while making evaluations realistic enough to capture adaptive attacks and deployment risks.