{
  "node_id": "owasp-llm-top-10-2025-llm09-misinformation",
  "title": "OWASP LLM Top 10 (2025) - LLM09:2025 Misinformation",
  "domain": "AI Governance & Law",
  "version": "2.0.0",
  "last_updated": "2024-11-17",
  "bluf": "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.",
  "paywall": {
    "status": "LOCKED",
    "unlock_cost_usd": "0.01",
    "skyfire_id": "41779894-ece2-4163-9761-b3b1b76e19b0"
  },
  "crosswalks": {
    "_available_keys": [
      "nist_framework",
      "iso_standard",
      "industry_mapping",
      "ai_overlay_2026"
    ],
    "_note": "Full crosswalk values included in vault response"
  },
  "dependencies": [
    "owasp-llm-top-10-2025",
    "nist-ai-rmf-1-0",
    "iso-42001-risk-assess",
    "nist-sp-800-53-r5"
  ],
  "primary_citations_count": 12
}