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Threat modeling LLM apps
Practical AI: Machine Learning, Data Science, LLM
Secure the Output, Fortify the Input
To secure LLM applications, focus on output validation as the primary step, ensuring harmful content checks and format validations are thorough, especially regarding links, markdown, and executable code. Outputs should be scrutinized to prevent exploitation through prompt injection that could leak sensitive user information. In parallel, implement strong input controls that restrict inappropriate queries and ensure the model's responses remain relevant and secure. By addressing output security first and then establishing rigid input validation frameworks, organizations can more safely deploy GenAI applications, mitigating complex vulnerabilities effectively.
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