Paper Publications

Affiliation of Author(s):[1] University of Electronic Science and Technology of China, China; [2] CISPA Helmholtz Center for Information Security, Germany; [3] NetApp

Journal:Proceedings of the 33rd USENIX Security Symposium

Key Words:Benchmarking - Classification (of information) - Natural language processing systems - Syntactics

Abstract:The increasing demand for customized Large Language Models (LLMs) has led to the development of solutions like GPTs. These solutions facilitate tailored LLM creation via natural language prompts without coding. However, the trustworthiness of third-party custom versions of LLMs remains an essential concern. In this paper, we propose the first instruction backdoor attacks against applications integrated with untrusted customized LLMs (e.g., GPTs). Specifically, these attacks embed the backdoor into the custom version of LLMs by designing prompts with backdoor instructions, outputting the attacker＇s desired result when inputs contain the predefined triggers. Our attack includes 3 levels of attacks: word-level, syntax-level, and semantic-level, which adopt different types of triggers with progressive stealthiness. We stress that our attacks do not require fine-tuning or any modification to the backend LLMs, adhering strictly to GPTs development guidelines. We conduct extensive experiments on 6 prominent LLMs and 5 benchmark text classification datasets. The results show that our instruction backdoor attacks achieve the desired attack performance without compromising utility. Additionally, we propose two defense strategies and demonstrate their effectiveness in reducing such attacks. Our findings highlight the vulnerability and the potential risks of LLM customization such as GPTs. ? USENIX Security Symposium 2024.All rights reserved.

Document Type:Conference article (CA)

Page Number:1849-1866

Translation or Not:no