Jailbreaking to Jailbreak

• URL: http://arxiv.org/abs/2502.09638v1
• Date: Sun, 09 Feb 2025 20:49:16 GMT
• Title: Jailbreaking to Jailbreak
• Authors: Jeremy Kritz, Vaughn Robinson, Robert Vacareanu, Bijan Varjavand, Michael Choi, Bobby Gogov, Scale Red Team, Summer Yue, Willow E. Primack, Zifan Wang,
• Abstract summary: We present a novel LLM-as-red-teamer approach in which a human jailbreaks a refusal-trained LLM to make it willing to jailbreak itself or other LLMs.<n>Our work not only introduces a strategic approach to red teaming, drawing inspiration from human red teamers, but also highlights jailbreaking-to-jailbreak as an overlooked failure mode of the safeguard.
• Score: 7.462595078160592
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Refusal training on Large Language Models (LLMs) prevents harmful outputs, yet this defense remains vulnerable to both automated and human-crafted jailbreaks. We present a novel LLM-as-red-teamer approach in which a human jailbreaks a refusal-trained LLM to make it willing to jailbreak itself or other LLMs. We refer to the jailbroken LLMs as $J_2$ attackers, which can systematically evaluate target models using various red teaming strategies and improve its performance via in-context learning from the previous failures. Our experiments demonstrate that Sonnet 3.5 and Gemini 1.5 pro outperform other LLMs as $J_2$, achieving 93.0% and 91.0% attack success rates (ASRs) respectively against GPT-4o (and similar results across other capable LLMs) on Harmbench. Our work not only introduces a scalable approach to strategic red teaming, drawing inspiration from human red teamers, but also highlights jailbreaking-to-jailbreak as an overlooked failure mode of the safeguard. Specifically, an LLM can bypass its own safeguards by employing a jailbroken version of itself that is willing to assist in further jailbreaking. To prevent any direct misuse with $J_2$, while advancing research in AI safety, we publicly share our methodology while keeping specific prompting details private.

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