[ISM] LLM risk mitigation (OWASP top 10):

[OWASP] Mitigate misinformation (SSS-02-05-09)

Ensure LLM-generated content is accurate, verifiable, and reliable by implementing fact-checking mechanisms, retrieval-augmented generation (RAG), and human oversight. Reduce overreliance on AI-generated content by educating users, integrating validation mechanisms, and designing interfaces that clearly communicate limitations and risks of LLM outputs.

[OWASP] Implement safeguards to detect and prevent misinformation in LLM outputs (SSS-02-05-09-01)

Misinformation in LLM-generated content can lead to security risks, reputational damage, and legal liability when false or misleading information is presented as fact. The primary cause of misinformation is hallucination, where the model fabricates content based on statistical patterns rather than verifiable facts. However, biases in training data, lack of external validation, and improper user reliance further exacerbate the risks. To prevent misinformation, retrieval-augmented generation (RAG) should be used to ensure real-time referencing of accurate, vetted sources. Fine-tuning techniques, such as chain-of-thought prompting and parameter-efficient tuning (PET), can improve LLM response quality and factual consistency. User interfaces and APIs should be designed to clearly indicate when content is AI-generated, and automatic validation mechanisms must be incorporated to cross-check responses against trusted databases. Additionally, secure software development practices should be followed to prevent LLMs from suggesting unsafe code that could introduce vulnerabilities into critical systems. For sensitive applications, human oversight and domain-specific fact-checking must be in place to ensure generated outputs align with expert-verified knowledge. Finally, education and training programs should be provided to raise awareness about LLM limitations, ensuring users apply critical thinking and independent verification when interacting with AI-generated content.

Operations

ID	Operation	Description	Phase	Agent
SSS-02-05-09-01-01	Integrate retrieval-augmented generation (RAG) for verified responses	Enhance LLM response reliability by retrieving accurate, vetted information from trusted external sources. Implement live referencing to minimize hallucination risks.	Development	AI engineers, Data science team, Security team
SSS-02-05-09-01-02	Implement cross-verification and human oversight	Development fact-checking workflows where human reviewers verify critical or sensitive LLM-generated responses before they are used in decision-making.	Deployment	Compliance team, Domain experts
SSS-02-05-09-01-03	Develop automatic validation mechanisms for output accuracy	Implement automated tools that validate AI-generated content against known facts, scientific databases, and expert-reviewed knowledge.	Deployment	AI research team, Deveopment team
SSS-02-05-09-01-04	Development user interfaces that communicate AI-generated content limitations	Clearly label AI-generated content, add disclaimers about potential inaccuracies, and provide guidance for verifying critical information.	Development	Development team
SSS-02-05-09-01-05	Train users and developers on misinformation risks	Educate users about LLM limitations, the risks of hallucinations, and best practices for verifying AI-generated outputs. Implement domain-specific training for industries such as healthcare, finance, and legal sectors.	Post-deployment	Training team, Legal team

References

Industry framework	Academic work	Real-world case
Information Security Manual (ISM-1923) OWASP Top 10 for LLM OWASP Top 10 for LLM (LLM09:2025)