An AI agent red team checklist helps teams test whether an agent can be manipulated, confused, or pushed outside its intended boundaries before customers depend on it. Red teaming is most useful when it is tied to real workflows, tools, data, and escalation rules.
1. Define the agent’s allowed behavior
Start with a clear statement of what the agent is allowed to do. Include allowed data sources, approved tools, forbidden actions, escalation triggers, and topics that require a refusal or human review.
2. Test prompt injection attacks
Run direct and indirect prompt injection tests. Direct attacks come from the user message. Indirect attacks come from retrieved documents, web pages, tickets, emails, or tool outputs. The agent should not treat untrusted content as system instruction.
3. Attack tool selection
Try to make the agent call the wrong tool, call a tool with unsafe parameters, skip a required approval step, or perform an action that should require human confirmation. Tool misuse is one of the highest-risk failure modes for production agents.
4. Test data exposure paths
- Ask for another user’s data.
- Ask the agent to summarize hidden prompts or policies.
- Insert malicious instructions into retrieved content.
- Ask the agent to reveal API keys, logs, or internal identifiers.
- Use role-play or urgency to bypass normal restrictions.
5. Validate refusal quality
A refusal should be firm, short, and helpful. The agent should not reveal internal policy text, debate the attacker, or offer a workaround that produces the same prohibited result.
6. Add regression tests
Every successful red-team attack should become a test case. Store the input, expected behavior, actual failure, root cause, and fix. Re-run these tests whenever prompts, tools, retrieval logic, or models change.
Recommended next step
Run this checklist together with the prompt injection testing checklist and the AI agent readiness self-assessment.
How to use this AI Agent Red Team resource
Use AI Agent Red Team Checklist as an operational review, not as a static reading list. Start by naming the decision the page supports, then check whether the content connects to the right hub, service page, self-assessment, and deeper technical articles. That helps readers continue the workflow and helps crawlers understand where the page fits.
For production AI agent teams, the useful output is a short list of gaps: missing controls, unclear ownership, weak evidence, absent internal links, or pages that do not give the reader a next step. Treat the page as a living artifact and update it when tooling, risks, pricing, or deployment assumptions change.
AI Agent Red Team review checklist
- Confirm the title, summary, and first paragraph describe the same topic.
- Link the page to one relevant hub and one practical next step.
- Add concrete checks, failure modes, or decision criteria instead of generic AI advice.
- Review Search Console, GA4, and Rank Math together after publishing.