Siren: A Learning-Based Multi-Turn Attack Framework for Simulating Real-World Human Jailbreak Behaviors
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908648640348160 |
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| author | Zhao, Yi Zhang, Youzhi |
| author_facet | Zhao, Yi Zhang, Youzhi |
| contents | Large language models (LLMs) are widely used in real-world applications, raising concerns about their safety and trustworthiness. While red-teaming with jailbreak prompts exposes the vulnerabilities of LLMs, current efforts focus primarily on single-turn attacks, overlooking the multi-turn strategies used by real-world adversaries. Existing multi-turn methods rely on static patterns or predefined logical chains, failing to account for the dynamic strategies during attacks. We propose Siren, a learning-based multi-turn attack framework designed to simulate real-world human jailbreak behaviors. Siren consists of three stages: (1) MiniMax-driven training set construction utilizing Turn-Level LLM feedback, (2) post-training attackers with supervised fine-tuning (SFT) and direct preference optimization (DPO), and (3) interactions between the attacking and target LLMs. Experiments demonstrate that Siren achieves an attack success rate (ASR) of 90% with LLaMA-3-8B as the attacker against Gemini-1.5-Pro as the target model, and 70% with Mistral-7B against GPT-4o, significantly outperforming single-turn baselines. Moreover, Siren with a 7B-scale model achieves performance comparable to a multi-turn baseline that leverages GPT-4o as the attacker, while requiring fewer turns and employing decomposition strategies that are better semantically aligned with attack goals. We hope Siren inspires the development of stronger defenses against advanced multi-turn jailbreak attacks under realistic scenarios. Code is available at https://github.com/YiyiyiZhao/siren. Warning: This paper contains potentially harmful text. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_14250 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Siren: A Learning-Based Multi-Turn Attack Framework for Simulating Real-World Human Jailbreak Behaviors Zhao, Yi Zhang, Youzhi Computation and Language Artificial Intelligence Cryptography and Security Large language models (LLMs) are widely used in real-world applications, raising concerns about their safety and trustworthiness. While red-teaming with jailbreak prompts exposes the vulnerabilities of LLMs, current efforts focus primarily on single-turn attacks, overlooking the multi-turn strategies used by real-world adversaries. Existing multi-turn methods rely on static patterns or predefined logical chains, failing to account for the dynamic strategies during attacks. We propose Siren, a learning-based multi-turn attack framework designed to simulate real-world human jailbreak behaviors. Siren consists of three stages: (1) MiniMax-driven training set construction utilizing Turn-Level LLM feedback, (2) post-training attackers with supervised fine-tuning (SFT) and direct preference optimization (DPO), and (3) interactions between the attacking and target LLMs. Experiments demonstrate that Siren achieves an attack success rate (ASR) of 90% with LLaMA-3-8B as the attacker against Gemini-1.5-Pro as the target model, and 70% with Mistral-7B against GPT-4o, significantly outperforming single-turn baselines. Moreover, Siren with a 7B-scale model achieves performance comparable to a multi-turn baseline that leverages GPT-4o as the attacker, while requiring fewer turns and employing decomposition strategies that are better semantically aligned with attack goals. We hope Siren inspires the development of stronger defenses against advanced multi-turn jailbreak attacks under realistic scenarios. Code is available at https://github.com/YiyiyiZhao/siren. Warning: This paper contains potentially harmful text. |
| title | Siren: A Learning-Based Multi-Turn Attack Framework for Simulating Real-World Human Jailbreak Behaviors |
| topic | Computation and Language Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2501.14250 |