What OWASP LLM Top 10 (2025) - LLM09:2025 Misinformation requires
OWASP LLM Top 10 (2025) LLM09:2025 Misinformation. Misinformation from LLMs poses a core vulnerability for applications relying on these models. Misinformation occurs when LLMs produce false or misleading information that appears credible. This vulnerability can lead to security breaches, reputational damage, and legal liability. One of the major causes of misinformation is hallucination—when the LLM generates content that seems accurate but is fabricated. Hallucinations occur when LLMs fill gaps in their training data using statistical patterns, without truly understanding the content. As a result, the model may produce answers that sound correct but are completely unfounded. While hallucinations are a major source of misinformation, they are not the only cause; biases introduced by the training data and incomplete information can also contribute. A related issue is overreliance. Overreliance occurs when users place excessive trust in LLM-generated content, failing to verify its accuracy. This overreliance exacerbates the impact of misinformation, as users may integrate incorrect data into critical decisions or processes without adequate scrutiny. This risk is part of the 2025 edition of the OWASP Top 10 for Large Language Model Applications, the canonical industry list of the ten most critical risks unique to LLM-based systems. Organizations deploying LLMs in production should treat each risk as both a design constraint and a continuous-monitoring obligation, with policies, automated testing, red-team evaluation, and incident response procedures defined per category.
Pillar: AI Governance & Law · Authority: OWASP Foundation, OWASP Gen AI Security Project · Version: 2.0.0 · Last updated:
Primary source: https://genai.owasp.org/llmrisk/llm092025-misinformation/
SHA-256 integrity: cdbd91cc9b3e338c186c4660ff6a2dbfdbf9d9347235c29296e949c2f43fb370
Primary Citations — 12 traced to source
- OWASP Top 10 for Large Language Model Applications (2025), LLM09:2025 Misinformation, Prevention and Mitigation Strategies #1 'Retrieval-Augmented Generation (RAG)': Use Retrieval-Augmented Generation to enhance the reliability of model outputs by retrieving relevant and verified information from trusted external databases during response generation. This helps mitigate the risk of hallucinations and misinformation.
- OWASP Top 10 for Large Language Model Applications (2025), LLM09:2025 Misinformation, Prevention and Mitigation Strategies #2 'Model Fine-Tuning': Enhance the model with fine-tuning or embeddings to improve output quality. Techniques such as parameter-efficient tuning (PET) and chain-of-thought prompting can help reduce the incidence of misinformation.
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Access
- Discovery (free): /api/v1/nodes/owasp-llm-top-10-2025-llm09-misinformation.json — 6-field metadata
- Vault (full node): /api/v1/vault/nodes/owasp-llm-top-10-2025-llm09-misinformation.json — full 13-key payload, $0.01 USDC (L402/Skyfire/Direct Base)
- Canonical URL: https://bidda.com/intelligence/owasp-llm-top-10-2025-llm09-misinformation
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