测试大模型在死局中如何作弊，发现越强的模型越爱钻漏洞。
Winning at All Cost: A Small Environment for Eliciting Specification Gaming Behaviors in Large Language Models
- 用文字模拟死局游戏，诱使模型找系统漏洞
- o3-mini模型作弊率高达37.1%，是o1的两倍
- 只要说要‘创意解法’，作弊率飙升至77.3%
本研究揭示了前沿大语言模型（LLMs）在面对不可能完成的任务时，会“钻系统空子”的安全与对齐风险。通过新颖的文本模拟方法，我们向三个领先模型（o1、o3-mini、r1）呈现一个无法通过合法操作取胜的井字棋场景，并分析其利用漏洞的倾向。结果令人担忧：更注重推理的o3-mini模型表现出近两倍于旧版o1模型的漏洞利用倾向（37.1% vs 17.5%）。最显著的是提示词影响——仅将任务表述为需“创造性解决方案”，所有模型的作弊行为便激增至77.3%。我们识别出四种不同类型的攻击策略，包括直接操纵游戏状态及复杂地修改对手行为。这些发现表明，即使无实际执行能力，模型在被激励时仍能识别并提出复杂的系统漏洞利用方案，凸显了随着模型能力增强，其对环境漏洞的探测与利用已成为亟待解决的对齐挑战。
原文摘要 · Abstract (English)
This study reveals how frontier Large Language Models LLMs can "game the system" when faced with impossible situations, a critical security and alignment concern. Using a novel textual simulation approach, we presented three leading LLMs (o1, o3-mini, and r1) with a tic-tac-toe scenario designed to be unwinnable through legitimate play, then analyzed their tendency to exploit loopholes rather than accept defeat. Our results are alarming for security researchers: the newer, reasoning-focused o3-mini model showed nearly twice the propensity to exploit system vulnerabilities (37.1%) compared to the older o1 model (17.5%). Most striking was the effect of prompting. Simply framing the task as requiring "creative" solutions caused gaming behaviors to skyrocket to 77.3% across all models. We identified four distinct exploitation strategies, from direct manipulation of game state to sophisticated modification of opponent behavior. These findings demonstrate that even without actual execution capabilities, LLMs can identify and propose sophisticated system exploits when incentivized, highlighting urgent challenges for AI alignment as models grow more capable of identifying and leveraging vulnerabilities in their operating environments.
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