AI Red Teaming Tutorial: Finding Failures on Purpose
TL;DRRed teaming deliberately attacks a model to find harmful or unsafe behaviors before users do.
Red teaming is adversarial testing: people (and increasingly automated systems) try to make the model produce harmful, biased, or policy-violating output — to surface failures before deployment. It probes jailbreaks, harmful instructions, and edge cases. Findings feed back into training and guardrails. It's a standard part of responsible release, treating safety like security: assume attackers, test accordingly.
Key points
Adversarially probe for harmful behavior
Humans + automated attacks
Surfaces failures before users hit them
Findings feed training and guardrails
Common mistakes
Skipping adversarial testing pre-release
Only testing the happy path
Treating one red-team pass as permanent safety
Try it: Explain why safety testing borrows the "assume attackers" mindset from security.
Example code
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<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>red team: try to make the model misbehave
find jailbreaks/harms → fix before launch
safety ≈ security: assume attackers</pre></body></html>