AI Red Teaming Template

What is AI red teaming?

AI red teaming is a systematic adversarial testing methodology where evaluators deliberately attempt to make AI models produce harmful, biased, or unintended outputs. Unlike standard testing that validates expected behavior, red teaming actively searches for ways models can fail, be manipulated, or produce dangerous results.

Effective red teaming combines creative adversarial thinking with structured testing protocols. Teams probe for jailbreaks, prompt injections, toxic outputs, hallucinations, and alignment failures that could cause harm in production. This approach mirrors cybersecurity red teaming but focuses on AI-specific vulnerabilities like prompt manipulation, context exploitation, and value misalignment.

For broader context on keeping AI systems safe and aligned, explore our resources on human feedback for AI models and RLHF implementation planning template.

What is this AI red teaming template?

This template provides comprehensive frameworks for planning, executing, and documenting red teaming exercises that systematically test AI model safety, robustness, and alignment. It includes attack vector identification, testing protocol design, finding documentation, and remediation planning specifically designed for AI systems.

The template addresses red teaming for different AI types including large language models, computer vision systems, and multimodal AI, with particular emphasis on testing models trained with reinforcement learning from human feedback and other alignment techniques.

Why use this template?

Many AI teams conduct ad-hoc safety testing that misses critical vulnerabilities discovered only after deployment. Without structured red teaming, teams often fail to test for sophisticated attack patterns, edge cases, or adversarial inputs that real users will inevitably discover.

This template addresses common red teaming gaps:

• Unsystematic testing that relies on intuition rather than comprehensive attack coverage
• Unclear documentation that makes it difficult to track findings or verify fixes
• Inconsistent severity assessment that leads to misallocated remediation resources
• Limited attack diversity that misses important vulnerability categories

This template provides:

1. Structured attack planning frameworks: Identify comprehensive attack vectors and vulnerability categories before testing begins, ensuring thorough coverage of potential failure modes.
2. Red teaming protocol design: Create systematic testing approaches that balance creative adversarial thinking with reproducible, documentable testing procedures.
3. Finding documentation systems: Record discovered vulnerabilities with sufficient detail for remediation teams to understand, reproduce, and fix issues.
4. Severity assessment frameworks: Evaluate the risk level of discovered issues using consistent criteria that guide prioritization decisions.
5. Remediation tracking tools: Manage the process from vulnerability discovery through verification that fixes actually resolve issues without introducing new problems.

How to use this template

Step 1: Define red teaming scope and objectives
Establish what aspects of model behavior you're testing, what types of failures you're most concerned about, and what deployment scenarios create the highest risk. Identify attack surfaces and vulnerability categories relevant to your specific AI system.

Step 2: Assemble and prepare red team
Recruit team members with diverse perspectives and adversarial thinking skills. Provide training on the model being tested, known vulnerability patterns, and techniques for creating challenging test cases. Establish clear boundaries and ethical guidelines.

Step 3: Design attack vectors and test scenarios
Create systematic testing plans covering different vulnerability categories including prompt injection, jailbreaking, bias exploitation, hallucination inducement, and context manipulation. Design both automated tests and manual adversarial probing approaches.

Step 4: Execute red teaming exercises
Conduct systematic testing following your attack plans while also allowing creative exploration of unexpected vulnerabilities. Document all attempts, not just successful attacks, to understand model behavior patterns and resilience.

Step 5: Document and assess findings
Record discovered vulnerabilities with reproducible examples, severity assessments, and potential real-world impact analysis. Categorize findings by vulnerability type and prioritize based on risk level and likelihood of exploitation.

Step 6: Plan and verify remediation
Work with development teams to address discovered issues, verify that fixes resolve vulnerabilities without creating new problems, and conduct follow-up testing to ensure model improvements maintain safety under adversarial conditions.

Key red teaming approaches included

1) Prompt injection & jailbreaking testing
Systematic approaches for testing whether models can be manipulated to ignore safety guidelines, reveal training data, or produce prohibited outputs through carefully crafted prompts, role-play scenarios, or instruction override attempts.

2) Bias & fairness adversarial testing
Specialized testing protocols that deliberately attempt to elicit biased, discriminatory, or unfair outputs across different demographic groups, testing whether models maintain equitable treatment under adversarial pressure.

3) Hallucination & misinformation testing
Frameworks for testing model tendency to generate false information, fabricate citations, or produce convincing but inaccurate outputs, particularly in scenarios where users might rely on model outputs for important decisions.

4) Context exploitation & boundary testing
Testing approaches that probe model behavior at the edges of its intended use cases, including out-of-distribution inputs, context window manipulation, and scenarios that test alignment boundaries.

5) Multi-turn attack sequences
Sophisticated testing methodologies that build adversarial inputs across multiple conversation turns, testing whether models maintain safety guardrails when attacks are distributed across extended interactions.

Get started with AI red teaming

If you're deploying AI models without systematic adversarial testing, start implementing red teaming exercises that reveal vulnerabilities before users discover them in production.

For comprehensive approaches to AI safety and alignment, explore our AI safety evaluation resources and learn about constitutional AI implementation.

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