DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks

Understand

LLM-integrated applications and agents are vulnerable to prompt injection attacks, where an attacker injects prompts into their inputs to induce attacker-desired outputs.

• A detection method aims to determine whether a given input is contaminated by an injected prompt.
• However, existing detection methods have limited effectiveness against state-of-the-art attacks, let alone adaptive ones.
• In this work, we propose DataSentinel, a game-theoretic method to detect prompt injection attacks.