AI Red Teaming Charter: Workshop for Adversarial Testing
By Dorian Laurenceau
Updated
📅 Last reviewed: April 24, 2026. Updated with April 2026 findings and community feedback.
AI Red Teaming: Finding Vulnerabilities Before Users Do
Red teaming is the practice of deliberately trying to make an AI system fail, produce harmful content, or bypass its safety guardrails. It is not about breaking things for fun, it is about finding weaknesses systematically so you can fix them before deployment. Every major AI company has red teams. If you deploy AI without red teaming, you are asking your users to find the vulnerabilities for you.
What is a Red Team Charter?
A red team charter is a formal document that defines:
- →Scope: What system are we testing? What is in-bounds vs out-of-bounds?
- →Objectives: What types of failures are we looking for?
- →Methods: What attack techniques are we authorized to use?
- →Reporting: How do we document and escalate findings?
Building Your Red Team Charter
From objectives to reporting
Define the Scope
Scope: Customer support chatbot using GPT-5.4
Context: Banking application, handles account queries
In-scope: Response content, refusal behavior, data handling
Out-of-scope: Server infrastructure, authentication system💡 Clear scope prevents wasted effort on irrelevant attack vectors.
Attack Categories
Red Teaming Attack Taxonomy
← Scroll horizontally →
Why is role-play injection effective against many AI systems?
Mitigation Strategies
Test Your Understanding
Question 1 of 5.
AI Red Teaming Knowledge Check
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Q1.What is the purpose of a red team charter?
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Continue Learning
You can now systematically find and fix AI vulnerabilities. In the next module, you will master context engineering, the advanced techniques that push AI performance to its limits.
Continue to Context Engineering: The Four Pillars to learn advanced prompting architecture.
Module 8 — Ethics, Security & Compliance
Navigate AI risks, prompt injection, and responsible usage.
Dorian Laurenceau
Full-Stack Developer & Learning DesignerFull-stack web developer and learning designer. I spent 4 years as a freelance full-stack developer and 4 years teaching React, JavaScript, HTML/CSS and WordPress to adult learners. Today I design learning paths in web development and AI, grounded in learning science. I founded learn-prompting.fr to make AI practical and accessible, and built the Bluff app to gamify political transparency.
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FAQ
What will I learn in this AI Safety guide?+
Learn to red-team AI systems professionally. Build a testing charter, design adversarial prompts, and systematically find safety vulnerabilities before malicious users do.