  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.
