Model-Based Input Validation for Preventing Prompt Injection Attacks

Abstract

As Large Language Models (LLMs) become increasingly integrated into various applications, the threat of prompt injection attacks has emerged as a significant security concern. This presentation introduces a novel model-based input validation approach to mitigate these attacks in LLM-integrated applications.

We present a meta-prompt methodology that acts as an intermediate validator, examining user inputs before they reach the LLM. Our approach builds on established input validation techniques, drawing parallels with traditional security measures like SQL injection prevention.

Throughout the presentation, we will discuss the challenges of input validation in LLM contexts and explore how our model-based approach provides a more flexible and adaptive solution. We’ll share preliminary results from evaluations against established prompt injection datasets, highlighting the effectiveness of our methodology in detecting and mitigating various types of injection attempts.

Join us for an insightful exploration of this innovative approach to enhancing the security of LLM applications through advanced prompt engineering techniques, and learn how to implement robust input validation mechanisms to safeguard your AI-driven systems.