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Bug bounty

As explained in Introduction to Generative AI, Second Edition

A bug-bounty program is an organized program that rewards people for reporting security defects or other system vulnerabilities.

A bug bounty is an organized contest or reporting program that rewards people for finding security defects or other vulnerabilities. In large-language-model testing, providers use bounties to invite outsiders to discover ways models can produce unsafe or otherwise undesirable outputs. section 5.9

Bug bounties extend red teaming beyond an internal team. They add crowdsourced human testing, helping providers find diverse attacks that automated systems may miss, especially across creative or multi-turn interactions. Their findings provide useful signals, not complete solutions: model failures can be probabilistic, difficult to reproduce, or resistant to straightforward fixes. section 5.9 section 10.22

A bug-bounty program can:

  • Scale manual adversarial testing by inviting many participants to search for model weaknesses. section 5.9
  • Expose risks publicly, increasing shared visibility and accountability around deployed systems. section 10.22
  • Feed discovered failure modes into safety workflows such as prompt changes, dataset modifications, access restrictions, or architectural safeguards. section 10.22

How Introduction to Generative AI, Second Edition covers it

How deep it goes
Explains the program’s role and limits, but stops short of specifying submission rules, reward structures, or reporting procedures.
Examples
Uses model-provider contests, public red-team reports, and community programs as examples of crowdsourced testing.
What it assumes
Assumes familiarity with model weaknesses at a general level while defining bug bounties through plain-language red-team context.
What you can do after
Evaluate/understand how external vulnerability reports support iterative improvement without replacing mitigation.
How it connects
Connects bug bounties with red teaming, adversarial testing, vulnerability mitigation, and responsible AI accountability.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Introduction to Generative AI, Second Edition. The section links point to the parts of the book it draws on.