  With the rising popularity of Large Language Models (LLMs), assessing their
trustworthiness through security tasks has gained critical importance.
Regarding the new task of universal goal hijacking, previous efforts have
concentrated solely on optimization algorithms, overlooking the crucial role of
the prompt. To fill this gap, we propose a universal goal hijacking method
called POUGH that incorporates semantic-guided prompt processing strategies.
Specifically, the method starts with a sampling strategy to select
representative prompts from a candidate pool, followed by a ranking strategy
that prioritizes the prompts. Once the prompts are organized sequentially, the
method employs an iterative optimization algorithm to generate the universal
fixed suffix for the prompts. Experiments conducted on four popular LLMs and
ten types of target responses verified the effectiveness of our method.
