# LLM09 - Misinformation

> LLM generates inaccurate, biased, or hallucinated content treated as truth.

- **Category**: Owasp Llm
- **Subcategory**: top10_2025
- **Canonical URL**: https://designpattern.fyi/owasp_llm/llm09_misinformation/

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## Description
'**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.'



## Use Cases
Use when deploying LLMs for information retrieval, content generation, or decision support systems.





## Trade-offs


### Advantages

- Reduces misinformation risk

- Builds user trust

- Improves output reliability

- Supports responsible AI use




### Considerations & Drawbacks

- Cannot eliminate hallucinations entirely

- Fact-checking adds latency

- Confidence calibration is imperfect







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**Reference**: [Original Source](https://genai.owasp.org)