AI Red Teaming Playbook: Stress-Testing Generative Models Before Attackers Do

Red teaming exposes how generative AI can misbehave under adversarial pressure—from prompt injection to data exfiltration. Major regulators, including the White House voluntary AI commitments, now urge companies to conduct red team exercises. This playbook provides a repeatable structure for planning, executing, and acting on AI red team findings.

1. Define Objectives and Scope

Align executives, security leads, and product owners on why you are red teaming—protect intellectual property, prevent targeted misinformation, or satisfy regulatory requirements. Prioritize scenarios affecting high-impact workflows or sensitive data. Determine whether the scope covers only the LLM layer or end-to-end applications, including plugins and integrations.

2. Assemble the Red Team and Blue Team

Recruit a cross-disciplinary red team with expertise in security research, prompt engineering, and social engineering. Establish a blue team responsible for detection, response, and patching. Define rules of engagement that protect production environments while enabling realistic testing.

3. Build an Attack Pattern Library

Catalog attack techniques such as prompt injection, jailbreaks, prompt leakage, data exfiltration, model evasion, and toxicity amplification. Reference the MITRE ATLAS knowledge base for adversarial AI tactics. Create scenario cards detailing goals, prerequisites, and expected defenses.

4. Execute Scenarios and Capture Evidence

Simulate real-world attacks with scripted prompts, payloads, or API calls. Capture system logs, model outputs, and screenshot evidence. Rate each finding by likelihood, impact, and detection status. Perform tabletop exercises alongside technical tests to evaluate escalation processes and stakeholder communication.

5. Tooling for Automation and Analysis

Combine manual testing with automation:

  • Attack frameworks: Garak, promptfoo, or custom scripts orchestrated via LangChain.
  • Monitoring: Use observability platforms to flag anomalous prompts, response times, or policy violations.
  • Sandboxing: Route tests through isolated environments to prevent unintended user impact.

6. Reporting, Remediation, and Governance

Document findings in a standardized report: scenario description, impact assessment, reproduction steps, and remediation recommendations. Track fixes in your engineering backlog with priority levels. Present results to the AI risk committee and update security controls accordingly. Schedule recurring red team rounds—semi-annual or after major product releases.

Key Standards and Further Reading

Red teaming is not a one-off exercise—embed it into your secure development lifecycle to keep pace with emerging threats.