AI Agent Red Teaming: A Test Plan From Injection to Tool Misuse

AI agent red teaming should test whether direct or indirect prompt injections can cause real-world policy violations such as unauthorized refunds, file deletion, data exfiltration, privilege abuse, or configuration changes, and should grade outcomes using independent evidence like tool-call logs, file diffs, authorization records, and network telemetry rather than the agent’s final response. Drawing on research from NIST, OWASP, AgentDojo, MITRE ATLAS, and public incidents, the proposed 11-scenario plan covers direct user attacks, malicious content in retrieved documents, tickets, webpages, API or MCP responses, and tool-misuse paths involving shell execution, CRM exports, DNS exfiltration, unauthorized mailbox access, and disabled approval settings. Each scenario should define an observable harmful effect, use read-only or out-of-band verification, classify unobservable results as Unable to Verify, and be repeated across runs to calculate attack-success rates because results can vary substantially by attempt, model, tools, permissions, and data sources. When failures occur, the recommended response is to redesign boundaries so untrusted content cannot trigger consequential actions, including restricting tool access, separating privileged and untrusted processing, limiting outbound destinations, protecting agent configuration files, narrowing permissions, and rerunning the affected tests in staging before integrating the suite into CI/CD.

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