LLM09: Misinformation – FireTail Blog
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<p>Nov 21, 2025 – Lina Romero – In 2025, Artificial Intelligence is everywhere, and so are AI vulnerabilities. In fact, according to our research, these vulnerabilities are up across the board. The OWASP Top 10 list of Risks to LLMs can help teams track the biggest challenges facing AI security in our current landscape. Misinformation occurs when an LLM produces false or misleading information as credible data. This vulnerability is not only common but also can be catastrophic, leading to poor interactions, loss of productivity, misdirected flows, damaged reputations, and legal liability. AI misinformation is often a result of AI hallucination, which occurs when an LLM generates data that seems accurate but in reality, is not. While hallucinations are one of the biggest causes of Misinformation, they are not the only cause. Biases from training data or incomplete training information can also cause misinformation. Additionally, users may have over-reliance on the LLM responses, which leads to further misinformation because users will trust incorrect data without verifying the information with other sources.<br> Common examples of Misinformation in LLMs include:<br> Unsupported Claims: sometimes, LLMs can produce information that has no source and is completely fabricated. This can lead to a number of issues, particularly when this information is used in situations like a court of law. Factual Inaccuracies: LLMs often produce inaccurate statements that seem true, and perhaps are close to the truth but not completely true, and therefore, fly under the radar. Unsafe Code Generation: LLMs are now being used to generate code, but this code is often generated using shortcuts, weak practices, and a lack of strong security that can lead to breaches, and more. Misrepresentation of Expertise: LLMs can create the illusion of being well-versed in certain topics, such as healthcare or cybersecurity, when in reality they are not, and this leads to dangerous consequences when users take them at face value.<br> Mitigation:<br> There are a variety of steps security teams can take to mitigate Misinformation in LLMs. Model fine-tuning: Enhancing LLMs by tune-tuning or embedding can improve output accuracy and quality. Developers should use techniques such as parameter-efficient tuning (PET) and chain-of-thought prompting to safeguard their models against misinformation.<br> Retrieval-Augmented Generation: RAG can produce more reliable model outputs by retrieving information only from trusted, verified sources, which helps prevent the risk of AI hallucinations.<br> Input Validation and Prompt Quality: Make sure that inputs to the LLM are valid and well structured, to minimize the risk of unpredictable responses.<br> Automatic Validation Mechanisms: Security teams should implement processes that validate key outputs automatically, effectively filtering out misinformation before it reaches users.<br> Risk Communication: Identifying risks associated with LLMs and communicating these with users can prevent AI misinformation from spreading. Secure Coding Practices: Using best coding practices can help prevent incorrect code suggestions within an LLM.<br> Cross Verification: Users should be instructed that information obtained from an LLM should not be utilized without verification from a trusted source.<br> User Interface Design: Teams should design APIs and user interfaces that promote responsible LLM use by implementing content filters, labelling AI-generated content to encourage fact-checking, and more. Overall, the best defense against LLM Misinformation is common sense. Users should not believe everything they learn from AI-generated content, and education and awareness around this can be a huge step in preventing the spread of misinformation. However, security teams should also build checks and verifications into the design of their LLMs to mitigate risks of hallucinations and factual inaccuracies. Want to take charge of your AI security posture? 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