OWASP LLM09:2025 Misinformation

2026-05-06 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Intermediate, short

Summary

OWASP has identified LLM09:2025 Misinformation as a core vulnerability for applications using large language models, where models produce false or misleading yet credible information. This can lead to security breaches, reputational damage, and legal liability. A primary cause is hallucination, where LLMs fabricate content by filling data gaps using statistical patterns, often sounding correct but being unfounded. Other contributors include training data biases and incomplete information. The issue is exacerbated by user overreliance, where excessive trust in LLM outputs leads to unverified integration of incorrect data into critical decisions. Risks include factual inaccuracies (e.g., Air Canada chatbot lawsuit), unsupported claims (e.g., ChatGPT fabricating legal cases), misrepresentation of expertise (e.g., health chatbots), and unsafe code generation (e.g., suggesting insecure libraries).

Key takeaway

For AI Security Engineers developing or deploying LLM-powered applications, you must prioritize robust misinformation mitigation. Implement RAG and fine-tuning to improve output accuracy, and design user interfaces that clearly communicate LLM limitations. Your teams should also establish human oversight and training programs to prevent overreliance, thereby reducing legal and reputational risks associated with false or misleading AI-generated content.

Key insights

LLM misinformation, driven by hallucination and overreliance, poses significant security, reputational, and legal risks.

Principles

• LLMs can fabricate credible but false information.
• User overreliance amplifies misinformation impact.
• Misinformation risks extend beyond malicious attacks.

Method

Mitigation involves Retrieval-Augmented Generation (RAG), fine-tuning, cross-verification, automatic validation, risk communication, secure coding, and user interface design.

In practice

• Implement RAG to retrieve verified external data.
• Fine-tune models with PET or chain-of-thought prompting.
• Train users to cross-verify LLM outputs.

Topics

• LLM Misinformation
• Hallucinations
• User Overreliance
• Retrieval-Augmented Generation
• Model Fine-Tuning

Best for: AI Security Engineer, Software Engineer, Legal Professional

Related on AIssential

• Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity — LLMs are more easily misled by peer consensus than corrected, even with reasoning interventions.
• 10 AI Myths That Mislead Developers — AI models, especially LLMs, predict tokens statistically, lacking true understanding or meaning.
• Detecting Data Contamination in Large Language Models — Black-box MIAs are currently ineffective at reliably detecting data contamination in LLM training corpora.
• Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation — LLM-assisted counterspeech for combined hate and misinformation is most effective with a mixed, knowledge-driven strategy and expert human post-editing.
• The Internet of Probability — LLMs transform the internet into a probability machine, eroding truth and provenance through statistical generation and contaminated training data.
• 5 Practical Techniques to Detect and Mitigate LLM Hallucinations Beyond Prompt Engineering — LLM hallucinations are a system problem requiring multi-layered detection and mitigation beyond prompt engineering.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.