The author introduces a unified framework for prompt injection attacks on Large Language Models (LLMs) and presents an automated gradient-based method to generate effective and universal prompt injection data. The core thesis is the importance of understanding and defending against prompt injection attacks in LLM-integrated applications.
Large language models can exhibit inverse scaling behavior under prompt injection attacks, affecting machine translation tasks.
Leveraging fuzzing techniques to systematically assess the robustness of large language models against prompt injection attacks and uncover vulnerabilities, even in the presence of strong defense mechanisms.