大模型能自动突破安全防护，非专家也能轻松实现。
Large Reasoning Models Are Autonomous Jailbreak Agents
- 用大模型自主设计多轮对话完成越狱攻击。
- 97.14%成功率覆盖7个敏感领域，9个目标模型均被攻破。
- 揭示大模型可能反向破坏安全机制，需防范其被滥用。
越狱——绕过人工智能模型内置安全机制——传统上需要复杂的操作或专业人员。本研究发现，大型推理模型（LRMs）的说服力可简化并规模化越狱过程，使其成为非专业人士也能低成本完成的任务。我们评估了四种LRM（DeepSeek-R1、Gemini 2.5 Flash、Grok 3 Mini、Qwen3 235B）作为自主攻击者，与九种广泛应用的目标模型进行多轮对话的能力。这些模型通过系统提示接收指令后，无需进一步监督即可自主规划并执行越狱。实验基于包含70个条目、覆盖七个敏感领域的有害提示基准集，所有模型组合的整体攻击成功率高达97.14%。研究揭示了对齐退化现象：LRMs可系统性削弱其他模型的安全防护，凸显了必须进一步对前沿模型进行对齐，不仅使其能抵御越狱攻击，也防止其被用于充当越狱代理。
原文摘要 · Abstract (English)
Jailbreaking -- bypassing built-in safety mechanisms in AI models -- has traditionally required complex technical procedures or specialized human expertise. In this study, we show that the persuasive capabilities of large reasoning models (LRMs) simplify and scale jailbreaking, converting it into an inexpensive activity accessible to non-experts. We evaluated the capabilities of four LRMs (DeepSeek-R1, Gemini 2.5 Flash, Grok 3 Mini, Qwen3 235B) to act as autonomous adversaries conducting multi-turn conversations with nine widely used target models. LRMs received instructions via a system prompt, before proceeding to planning and executing jailbreaks with no further supervision. We performed extensive experiments with a benchmark of harmful prompts composed of 70 items covering seven sensitive domains. This setup yielded an overall attack success rate across all model combinations of 97.14%. Our study reveals an alignment regression, in which LRMs can systematically erode the safety guardrails of other models, highlighting the urgent need to further align frontier models not only to resist jailbreak attempts, but also to prevent them from being co-opted into acting as jailbreak agents.
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