How to protect your LLM against Prompt Injections

The Prompt Desk

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.
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