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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2501.07959 |
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| _version_ | 1866913673689169920 |
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| author | Hua, Jiaqi Wei, Wanxu |
| author_facet | Hua, Jiaqi Wei, Wanxu |
| contents | Recently, several works have been conducted on jailbreaking Large Language Models (LLMs) with few-shot malicious demos. In particular, Zheng et al. focus on improving the efficiency of Few-Shot Jailbreaking (FSJ) by injecting special tokens into the demos and employing demo-level random search, known as Improved Few-Shot Jailbreaking (I-FSJ). Nevertheless, we notice that this method may still require a long context to jailbreak advanced models e.g. 32 shots of demos for Meta-Llama-3-8B-Instruct (Llama-3) \cite{llama3modelcard}. In this paper, we discuss the limitations of I-FSJ and propose Self-Instruct Few-Shot Jailbreaking (Self-Instruct-FSJ) facilitated with the demo-level greedy search. This framework decomposes the FSJ attack into pattern and behavior learning to exploit the model's vulnerabilities in a more generalized and efficient way. We conduct elaborate experiments to evaluate our method on common open-source models and compare it with baseline algorithms. Our code is available at https://github.com/iphosi/Self-Instruct-FSJ. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07959 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Instruct Few-Shot Jailbreaking: Decompose the Attack into Pattern and Behavior Learning Hua, Jiaqi Wei, Wanxu Artificial Intelligence Recently, several works have been conducted on jailbreaking Large Language Models (LLMs) with few-shot malicious demos. In particular, Zheng et al. focus on improving the efficiency of Few-Shot Jailbreaking (FSJ) by injecting special tokens into the demos and employing demo-level random search, known as Improved Few-Shot Jailbreaking (I-FSJ). Nevertheless, we notice that this method may still require a long context to jailbreak advanced models e.g. 32 shots of demos for Meta-Llama-3-8B-Instruct (Llama-3) \cite{llama3modelcard}. In this paper, we discuss the limitations of I-FSJ and propose Self-Instruct Few-Shot Jailbreaking (Self-Instruct-FSJ) facilitated with the demo-level greedy search. This framework decomposes the FSJ attack into pattern and behavior learning to exploit the model's vulnerabilities in a more generalized and efficient way. We conduct elaborate experiments to evaluate our method on common open-source models and compare it with baseline algorithms. Our code is available at https://github.com/iphosi/Self-Instruct-FSJ. |
| title | Self-Instruct Few-Shot Jailbreaking: Decompose the Attack into Pattern and Behavior Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2501.07959 |