PROMPTFUZZ introduces a systematic testing framework that uses fuzzing techniques to identify and mitigate prompt injection vulnerabilities in Large Language Models.

• Employs mutation-based fuzzing to generate diverse attack vectors
• Develops evaluation metrics to measure LLM robustness against prompt injection
• Demonstrates effectiveness across multiple popular LLMs, revealing critical security gaps
• Provides actionable insights for developing more secure AI systems

This research is crucial for organizations deploying LLMs in sensitive environments where security breaches could have significant consequences, enabling proactive defense against emerging attack patterns.

PROMPTFUZZ: Harnessing Fuzzing Techniques for Robust Testing of Prompt Injection in LLMs