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LLM09 · OWASP LLM Top 10

Misinformation (LLM09)

An LLM generates incorrect or misleading content that the user trusts and acts on. Hallucination is the obvious case; sycophancy and manipulated outputs are subtler. Misinformation becomes a security risk when it leads to operational decisions, legal advice, or downstream automation that depends on accuracy.

Examples

  • An LLM-based legal-research tool cites cases that do not exist.
  • A chatbot's hallucinated security advice causes a customer to misconfigure access controls.
  • A coding assistant suggests a non-existent npm package, which an attacker then registers maliciously.

Recommended controls

  • Output validation against source-of-truth where possible
  • User-facing confidence indicators
  • Disclaimer and human-review workflows for high-stakes outputs
  • Hallucination testing as part of release

Posture Check checkpoint

Posture Check questions Q16–Q20. Affects Model.

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