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Owasp Llm
top10_2025
LLM09 - Misinformation
LLM generates inaccurate, biased, or hallucinated content treated as truth.
Intent & Description
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🎯 Intent
Minimize the risk of LLMs producing and disseminating false, misleading, or hallucinated information.
📋 Context
LLMs can generate convincing but factually incorrect content (hallucinations). Users may trust this output, leading to incorrect decisions, reputational damage, or safety issues.
💡 Solution
Implement retrieval-augmented generation for factual grounding. Use cross-referencing and fact-checking. Display confidence scores. Add disclaimers to AI-generated content. Enable user feedback mechanisms.'
Real-world Use Case
Use when deploying LLMs for information retrieval, content generation, or decision support systems.
📌 TL;DR
Combat LLM hallucinations. Use RAG for grounding, cross-reference facts, display confidence scores, add disclaimers.
Advantages
- Reduces misinformation risk
- Builds user trust
- Improves output reliability
- Supports responsible AI use
Disadvantages
- Cannot eliminate hallucinations entirely
- Fact-checking adds latency
- Confidence calibration is imperfect