In this episode, we discuss, how we might protect prompt-based applications and LLMs from prompt injection. We discuss how data validation was done in the 1960s and modern libraries and techniques that can successfully act as a first line of defense against prompt injection. We touch on the idea that using other types of models, such as decision trees, conventional NLP pipelines, embedding models, or neural networks trained on datasets different from typical LLM training data, might be used to validate inputs before sending them to an LLM.

—
Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.
Check out PromptDesk.ai for an open-source prompt management tool.
Check out Brad’s AI Consultancy at bradleyarsenault.me
Add Justin Macorin and Bradley Arsenault on LinkedIn.

Please fill out our listener survey here to help us create a better podcast: https://docs.google.com/forms/d/e/1FAIpQLSfNjWlWyg8zROYmGX745a56AtagX_7cS16jyhjV2u_ebgc-tw/viewform?usp=sf_link

Hosted on Ausha. See ausha.co/privacy-policy for more information.