Response Attack: Exploiting Contextual Priming to Jailbreak Large Language Models

• URL: http://arxiv.org/abs/2507.05248v1
• Date: Mon, 07 Jul 2025 17:56:05 GMT
• Title: Response Attack: Exploiting Contextual Priming to Jailbreak Large Language Models
• Authors: Ziqi Miao, Lijun Li, Yuan Xiong, Zhenhua Liu, Pengyu Zhu, Jing Shao,
• Abstract summary: Contextual priming, where earlier stimuli covertly bias later judgments, offers an unexplored attack surface for large language models (LLMs)<n>We propose Response Attack, which uses an auxiliary LLM to generate a mildly harmful response to a paraphrased version of the original malicious query.<n> RA consistently outperforms seven state-of-the-art jailbreak techniques, achieving higher attack success rates.
• Score: 17.860698041523918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contextual priming, where earlier stimuli covertly bias later judgments, offers an unexplored attack surface for large language models (LLMs). We uncover a contextual priming vulnerability in which the previous response in the dialogue can steer its subsequent behavior toward policy-violating content. Building on this insight, we propose Response Attack, which uses an auxiliary LLM to generate a mildly harmful response to a paraphrased version of the original malicious query. They are then formatted into the dialogue and followed by a succinct trigger prompt, thereby priming the target model to generate harmful content. Across eight open-source and proprietary LLMs, RA consistently outperforms seven state-of-the-art jailbreak techniques, achieving higher attack success rates. To mitigate this threat, we construct and release a context-aware safety fine-tuning dataset, which significantly reduces the attack success rate while preserving model capabilities. The code and data are available at https://github.com/Dtc7w3PQ/Response-Attack.

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