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LLM09—Misinformation

>Control Description

Misinformation from LLMs poses a core vulnerability when LLMs produce false or misleading information that appears credible. This can lead to security breaches, reputational damage, and legal liability. Hallucination, where LLMs generate fabricated content that seems accurate, is a major cause. Overreliance on LLM outputs without verification exacerbates the impact.

>Vulnerability Types

  • 1.Factual Inaccuracies: Model produces incorrect statements leading to decisions based on false information
  • 2.Unsupported Claims: Model generates baseless assertions harmful in healthcare or legal contexts
  • 3.Misrepresentation of Expertise: Model gives illusion of understanding complex topics misleadingly
  • 4.Unsafe Code Generation: Model suggests insecure or non-existent code libraries

>Common Impacts

Decisions based on false information
Legal liability from incorrect advice
Reputational damage
Security vulnerabilities from hallucinated packages
User harm from medical or financial misinformation

>Prevention & Mitigation Strategies

  1. 1.Use Retrieval-Augmented Generation (RAG) to enhance reliability with verified information
  2. 2.Enhance model with fine-tuning using techniques like PET and chain-of-thought prompting
  3. 3.Encourage cross-verification of LLM outputs with trusted external sources
  4. 4.Implement human oversight and fact-checking for critical information
  5. 5.Implement automatic validation mechanisms for key outputs
  6. 6.Identify and communicate risks and limitations of LLM-generated content to users
  7. 7.Establish secure coding practices to prevent integration of incorrect code suggestions
  8. 8.Design interfaces that encourage responsible use with content filters and clear labeling

>Attack Scenarios

#1Hallucinated Package Attack

Attackers experiment with coding assistants to find commonly hallucinated package names. They publish malicious packages with those names. Developers unknowingly integrate these packages, giving attackers unauthorized access.

#2Medical Chatbot Liability

A company provides a chatbot for medical diagnosis without ensuring sufficient accuracy. The chatbot provides poor information leading to patient harm. The company is successfully sued for damages.

>MITRE ATLAS Mapping

AML.T0048.002

>References

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