New Threat: Indirect Prompt Injection Exploits LLM-Integrated Apps | Learn How to Stay Safe!

A paper discusses the vulnerability of Large Language Models (LLMs) integrated into various applications to targeted adversarial prompting, such as Prompt Injection (PI) attacks. The authors introduce a new attack vector, Indirect Prompt Injection, which enables adversaries to remotely exploit LLM-integrated applications by strategically injecting prompts into data likely to be retrieved. The paper provides a comprehensive taxonomy of the impacts and vulnerabilities of these attacks, including data theft, worming, and information ecosystem contamination. The authors demonstrate the practical viability of their attacks against real-world systems and synthetic applications built on GPT-4. The paper aims to raise awareness of these vulnerabilities and promote the safe and responsible deployment of LLMs and the development of robust defenses against potential attacks. Comments discuss potential solutions, the difficulty of detecting prompt injection attacks, and the need for separating instruction and data channels.

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