Exposing the Achilles' Heel: Black-box Reverse Engineering of Commercial LLM Plugins' Hidden Prompts
Year: 2025
Type: conference-paper
Abstract: Large language models (LLMs) demonstrate powerful natural language semantics understanding capabilities and are widely integrated into applications. OpenAI provides a platform for developers to construct custom applications, extending ChatGPT's functions and integrating external tools. Since its release in November 2023, over 3 million custom applications have been created. However, such a vast ecosystem also conceals security and privacy threats. For developers, instruction leaking attacks threaten the intellectual property of instructions in LLM applications through carefully crafted adversarial prompts. To systematically evaluate the scope of threats in real-world LLM applications, we develop an inception prompt hijacking attack, namely IPH, target LLM applications. Our experiments on 5,000 real-world LLM applications reveal that over 95.1% of applications are vulnerable to instruction-leaking attacks via one or more adversarial prompts. Our findings raise awareness among LLM applications developers about the importance of integrating specific defensive strategies in their instructions.
Authors: Wenying Wei, Kaifa Zhao, Hao Zhou
Institution: Hong Kong Polytechnic University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412704382