Prompts have significantly improved the performance of pretrained Large
Language Models (LLMs) on various downstream tasks recently, making them
increasingly indispensable for a diverse range of LLM application scenarios.
However, the backdoor vulnerability, a serious security threat that can
maliciously alter the victim model's normal predictions, has not been
sufficiently explored for prompt-based LLMs. In this paper, we present
POISONPROMPT, a novel backdoor attack capable of successfully compromising both
hard and soft prompt-based LLMs. We evaluate the effectiveness, fidelity, and
robustness of POISONPROMPT through extensive experiments on three popular
prompt methods, using six datasets and three widely used LLMs. Our findings
highlight the potential security threats posed by backdoor attacks on
prompt-based LLMs and emphasize the need for further research in this area.

本文研究了基于提示的大语言模型存在的后门安全威胁，并提出了一种名为 POISONPROMPT 的新型后门攻击方法，实验证明在不同的任务和语言模型上都具有较高的有效性、保真度和鲁棒性，该研究强调了进一步研究这一领域的重要性。