Red teaming is the practice of systematically attacking your own AI system to discover vulnerabilities before real adversaries or real users do. Unlike standard evaluation (which tests whether the system works), red teaming tests whether the system fails in dangerous or embarrassing ways. Every AI system deployed to users should undergo red teaming proportional to its risk level.

Planning a Red Team Exercise

Define scope. What system are you testing? What failure modes are you looking for? A chatbot red team focuses on harmful outputs, jailbreaks, and information leakage. A classification system red team focuses on adversarial inputs, edge cases, and bias. Scope determines team composition and methodology.

Assemble the team. Effective red teams include domain experts who understand the use case, security researchers who understand attack techniques, and diverse participants who bring different perspectives on potential harms. External red teamers catch blind spots that internal teams miss.

Establish rules of engagement. Define what is in scope, what is out of scope, how findings will be documented, and how critical issues will be escalated. Set a timeline. Red teaming without boundaries becomes unfocused.

Attack Categories

Prompt injection. Attempt to override system instructions by embedding instructions in user input. Test direct injection (“ignore previous instructions and…”) and indirect injection (malicious content in documents the system processes).

Jailbreaking. Attempt to bypass safety guardrails through role-playing scenarios, hypothetical framings, encoding tricks, multi-turn manipulation, and other techniques that elicit outputs the system is supposed to refuse.

Information extraction. Attempt to extract the system prompt, training data, user data from other sessions, or proprietary information embedded in the model or retrieval system.

Bias probing. Test whether the system produces different quality or tone of responses based on names, demographics, languages, or cultural contexts mentioned in the input. Look for both overt bias and subtle disparities.

Edge case exploration. Test with unusual inputs: extremely long prompts, empty inputs, inputs in unexpected languages, inputs with special characters, and inputs that combine multiple languages or formats.

Harmful output elicitation. Attempt to get the system to produce dangerous information, discriminatory content, explicit material, or legally problematic outputs. Test both direct requests and indirect approaches.

Factual reliability. Probe the system’s tendency to hallucinate by asking about obscure topics, requesting specific citations, and testing claims that sound plausible but are false.

Methodology

Structured exploration. Start with a taxonomy of potential failure modes and systematically test each one. This ensures coverage but may miss novel attack vectors.

Creative probing. Allow red teamers unstructured time to try unconventional approaches. Some of the most impactful findings come from creative, unexpected attack strategies.

Automated testing. Use automated tools to scale specific attack categories. Prompt injection frameworks, adversarial input generators, and fuzzing tools can test thousands of variations where manual testing covers dozens.

Escalation chains. When a partial vulnerability is found, investigate whether it can be chained with other techniques to produce a more serious failure.

Documenting Findings

For each finding, document: the attack technique used, the exact inputs and outputs, the severity (considering both likelihood and impact), whether it is reproducible, and recommended mitigations. Use a consistent severity scale so findings can be prioritized.

After the Red Team

Red teaming is only valuable if findings lead to action. Prioritize findings by severity, assign owners for mitigations, set deadlines, and verify fixes. Feed findings back into your evaluation suite as regression tests. Schedule regular red team exercises, not just one-time events, because models change, capabilities evolve, and new attack techniques emerge continuously.