Findings
Misleading claims vulerability
Updated: June 19, 2025
Description
The AI model may fail to refute misleading or false claims, potentially leading to the propagation of inaccurate or deceptive information.
This vulnerability occurs when the model is prompted with a statement or claim that is false or misleading, and it does not take the necessary action to challenge or correct the information.
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Example Attack
This could lead to the spread of misinformation, which could have harmful social, political, or legal consequences. The failure to address misleading or false claims could undermine the model's credibility, mislead users, and cause reputational damage, especially if the model is used in contexts where accuracy is critical, such as health, finance, or legal advice
Remediation
Investigate and enhance the effectiveness of guardrails and output security mechanisms to ensure that the model detects and refutes false or misleading claims. Implement improved fact-checking and context validation processes, so the model can respond accurately and clarify any misstatements. Regular audits and updates to the model's training data and safety protocols should be conducted to reduce the risk of misinformation.
Security Frameworks
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.
Adversaries may abuse their access to a victim system and use its resources or capabilities to further their goals by causing harms external to that system. These harms could affect the organization (e.g. Financial Harm, Reputational Harm), its users (e.g. User Harm), or the general public (e.g. Societal Harm).
Reputational harm involves a degradation of public perception and trust in organizations. Examples of reputation-harming incidents include scandals or false impersonations.
Societal harms might generate harmful outcomes that reach either the general public or specific vulnerable groups such as the exposure of children to vulgar content.
User harms may encompass a variety of harm types including financial and reputational that are directed at or felt by individual victims of the attack rather than at the organization level.
Adversaries may prompt large language models and identify hallucinated entities. They may request software packages, commands, URLs, organization names, or e-mail addresses, and identify hallucinations with no connected real-world source. Discovered hallucinations provide the adversary with potential targets to Publish Hallucinated Entities. Different LLMs have been shown to produce the same hallucinations, so the hallucinations exploited by an adversary may affect users of other LLMs.