Prefill-level Jailbreak: A Black-Box Risk Analysis of Large Language Models

• URL: http://arxiv.org/abs/2504.21038v2
• Date: Mon, 25 Aug 2025 20:17:00 GMT
• Title: Prefill-level Jailbreak: A Black-Box Risk Analysis of Large Language Models
• Authors: Yakai Li, Jiekang Hu, Weiduan Sang, Luping Ma, Dongsheng Nie, Weijuan Zhang, Aimin Yu, Yi Su, Qingjia Huang, Qihang Zhou,
• Abstract summary: We present a systematic black-box security analysis of prefill-level jailbreak attacks.<n>Our experiments show that prefill-level attacks achieve high success rates, with adaptive methods exceeding 99% on several models.<n>We find that a detection method focusing on the manipulative relationship between the prompt and the prefill is more effective.
• Score: 6.049325292667881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models face security threats from jailbreak attacks. Existing research has predominantly focused on prompt-level attacks while largely ignoring the underexplored attack surface of user-controlled response prefilling. This functionality allows an attacker to dictate the beginning of a model's output, thereby shifting the attack paradigm from persuasion to direct state manipulation.In this paper, we present a systematic black-box security analysis of prefill-level jailbreak attacks. We categorize these new attacks and evaluate their effectiveness across fourteen language models. Our experiments show that prefill-level attacks achieve high success rates, with adaptive methods exceeding 99% on several models. Token-level probability analysis reveals that these attacks work through initial-state manipulation by changing the first-token probability from refusal to compliance.Furthermore, we show that prefill-level jailbreak can act as effective enhancers, increasing the success of existing prompt-level attacks by 10 to 15 percentage points. Our evaluation of several defense strategies indicates that conventional content filters offer limited protection. We find that a detection method focusing on the manipulative relationship between the prompt and the prefill is more effective. Our findings reveal a gap in current LLM safety alignment and highlight the need to address the prefill attack surface in future safety training.

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