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.
If you have questions at the intersection of Cybersecurity and AI, you need to know Donato at WithSecure! Donato has been threat modeling AI applications and seriously applying those models in his day-to-day work. He joins us in this episode to discuss his LLM application security canvas, prompt injections, alignment, and more.
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