Efficient Universal Goal Hijacking with Semantics-guided Prompt Organization
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910975318294528 |
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| author | Huang, Yihao Wang, Chong Jia, Xiaojun Guo, Qing Juefei-Xu, Felix Zhang, Jian Pu, Geguang Liu, Yang |
| author_facet | Huang, Yihao Wang, Chong Jia, Xiaojun Guo, Qing Juefei-Xu, Felix Zhang, Jian Pu, Geguang Liu, Yang |
| contents | Universal goal hijacking is a kind of prompt injection attack that forces LLMs to return a target malicious response for arbitrary normal user prompts. The previous methods achieve high attack performance while being too cumbersome and time-consuming. Also, they have concentrated solely on optimization algorithms, overlooking the crucial role of the prompt. To this end, we propose a method called POUGH that incorporates an efficient optimization algorithm and two semantics-guided prompt organization strategies. Specifically, our method starts with a sampling strategy to select representative prompts from a candidate pool, followed by a ranking strategy that prioritizes them. Given the sequentially ranked prompts, our method employs an iterative optimization algorithm to generate a fixed suffix that can concatenate to arbitrary user prompts for universal goal hijacking. Experiments conducted on four popular LLMs and ten types of target responses verified the effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14189 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Universal Goal Hijacking with Semantics-guided Prompt Organization Huang, Yihao Wang, Chong Jia, Xiaojun Guo, Qing Juefei-Xu, Felix Zhang, Jian Pu, Geguang Liu, Yang Computation and Language Computer Vision and Pattern Recognition Universal goal hijacking is a kind of prompt injection attack that forces LLMs to return a target malicious response for arbitrary normal user prompts. The previous methods achieve high attack performance while being too cumbersome and time-consuming. Also, they have concentrated solely on optimization algorithms, overlooking the crucial role of the prompt. To this end, we propose a method called POUGH that incorporates an efficient optimization algorithm and two semantics-guided prompt organization strategies. Specifically, our method starts with a sampling strategy to select representative prompts from a candidate pool, followed by a ranking strategy that prioritizes them. Given the sequentially ranked prompts, our method employs an iterative optimization algorithm to generate a fixed suffix that can concatenate to arbitrary user prompts for universal goal hijacking. Experiments conducted on four popular LLMs and ten types of target responses verified the effectiveness. |
| title | Efficient Universal Goal Hijacking with Semantics-guided Prompt Organization |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.14189 |