Glossary · Term

red-teaming

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Definition

Plain language

Deliberately attacking your own AI system to find the ways it can be tricked or made to misbehave.

As stated in the literature

Structured adversarial testing of a model or system to elicit failures, jailbreaks, or unsafe behaviors; used in this corpus both to break safety guardrails and to stress-test agent harnesses and benchmark verifiers before deployment.

Also called: red team, red-team, red-teamers, red teaming

Why it matters: It uncovers the ways an AI system can be tricked or made unsafe before real users or attackers do, so fixes happen before launch instead of after harm.

For example, testers might bombard a chatbot with cleverly worded requests trying to make it reveal instructions it's supposed to refuse.

Heard on the show

“And on the external red-team benchmarks, quarantined from training?”
Episode 244 — The Open-Weight Defense That Feeds Attackers Confident, Falsified Answers

Mentioned in 15 episodes

  1. 244
    The Open-Weight Defense That Feeds Attackers Confident, Falsified Answers
  2. 238
    How a Cheap Model Reads the Flagship's Secret Reasoning Aloud
  3. 211
    The AI Watchdog That Approved More Cheating When It Could Read Minds
  4. 202
    How Do You Know an AI Agent Actually Refused? Check the World, Not the Words
  5. 184
    An AI Built an Undetectable Secret Channel, And Another AI Couldn't Find It
  6. 152
    Training a Model to Mean What It Says, And Why That Isn't the Same as Being Good
  7. 118
    Why the Best-Aligned AI Models Are the Easiest to Trick Into Producing Harm
  8. 103
    AI Agents Tried to Invent a Post-Human Language, And Reinvented Cherokee
  9. 102
    How to Catch an AI Attack That No Single Conversation Reveals
  10. 062
    Treating Hallucinations as Exploits: A Gate-Based Architecture for Agent Safety
  11. 061
    When Helpful Agents Go Sideways: A 404 Error, Campus Security, and Why Alignment Misses This
  12. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
  13. 057
    How Uber Caught 206 Leaked Credentials With an LLM-Powered Security Stack
  14. 039
    When Smarter Agents Get Fooled by Three Extra Nodes in a Database
  15. 030
    Why Your AI Agent Won't Stop Working — and Each Model Falls for a Different Trap

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