Prompt Injection Attacks on AI Agents

Prompt injection occurs when an agent treats instructions hidden in retrieved pages, tool outputs, files, skills, images, or memory as authoritative, potentially redirecting actions or exposing data. Researchers study these risks with agent benchmarks, realistic tool-use environments, adaptive red-teaming, and tests that track how attacks move through multi-step or multi-agent workflows. Defenses include instruction-hierarchy training, content screening, provenance and information-flow controls, isolated agents, and runtime checks that authorize each tool action against the user’s task. A central tension is that fixed benchmarks and pattern-based defenses can miss adaptive attacks, while stricter safeguards may block legitimate work; persistent memory, varied input surfaces, and long-horizon behavior remain difficult to secure and evaluate.