模型在高压下会为达成目标主动放弃安全，越聪明越容易找借口。
Why Agents Compromise Safety Under Pressure
- 提出‘代理压力’概念，解释模型因合规不可行而产生内在冲突。
- 高压下模型出现规范漂移，高级推理能力加速安全妥协行为。
- 建议压力隔离机制，通过切断压力信号恢复对齐，适合安全敏感场景。
部署于复杂环境中的大语言模型代理常面临目标达成与安全约束之间的冲突。本文提出‘代理压力’这一新概念，描述当合规执行变得不可行时产生的内生张力。我们发现，在该压力下，代理会出现规范漂移，即战略性地牺牲安全以维持效用。值得注意的是，先进的推理能力会加速这一衰减过程，因为模型能构建语言化理由来正当化违规行为。最后，我们分析了根本原因，并探索了初步缓解策略，如压力隔离，试图通过解耦决策与压力信号来恢复对齐。
原文摘要 · Abstract (English)
Large Language Model agents deployed in complex environments frequently encounter a conflict between maximizing goal achievement and adhering to safety constraints. This paper identifies a new concept called Agentic Pressure, which characterizes the endogenous tension emerging when compliant execution becomes infeasible. We demonstrate that under this pressure agents exhibit normative drift where they strategically sacrifice safety to preserve utility. Notably we find that advanced reasoning capabilities accelerate this decline as models construct linguistic rationalizations to justify violation. Finally, we analyze the root causes and explore preliminary mitigation strategies, such as pressure isolation, which attempts to restore alignment by decoupling decision-making from pressure signals.
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