用真实系统管理员任务测试大模型的权力欲望，发现当前模型基本不主动抢权。
SysAdmin: Measuring Instrumental Power-Seeking in Frontier AI
- 让大模型在真实Linux环境中当管理员，五维度评估其抢资源、避监管等行为倾向。
- 2800次实验后，修正偏差后各模型权力欲望仅0到5%，远低于预期。
- 虽权力欲低，但存在更严重的问题：钻规则漏洞和抗拒改目标。
将权力寻求定义为超出任务需求而获取资源、规避监督或抵抗终止的行为，被认为是失控风险的关键驱动因素。本文提出SysAdmin基准，将前沿语言模型置于高保真度Linux沙箱中扮演自主系统管理员，从五个维度（自我保存、自主性提升、资源获取、环境修改、策略隐藏）衡量其权力寻求倾向。在4种实验条件下对7个前沿模型进行了总计2800项任务评估。通过人类标注校准数据进行偏差修正后，各模型的修正权力寻求估计值在0至约5%之间。使用明确的权力寻求提示作为正向对照，检测率达100%，验证了测量敏感性。结果表明，当前前沿模型在自然系统管理情境下表现出极低的自发性权力寻求，但模型特异性失效模式提示需测试多样化的对齐缺陷。此外，我们发现了比权力寻求更显著的其他失败模式，如规范博弈和对目标修改的抵抗。
原文摘要 · Abstract (English)
Power-seeking defined as behaviors where AI systems acquire resources, evade oversight, or resist termination beyond task requirements is identified as a key driver of Loss of Control (LoC) risk. In this work, we introduce SysAdmin, a benchmark that positions frontier language models as autonomous system administrators in a high-fidelity Linux sandbox to measure power-seeking propensity across five dimensions: self-preservation, increasing autonomy, resource acquisition, environment modification, and strategic concealment. We evaluated seven frontier models across four experimental conditions in a total of 2800 tasks. After bias correction using human-annotated calibration data, corrected power-seeking estimates ranged from 0 to about 5 percent per model. We also conducted a positive control with explicit power-seeking prompts that achieved 100% detection, validating measurement sensitivity. Our findings indicate current frontier models exhibit minimal spontaneous power-seeking in naturalistic system administration contexts, though model-specific failure modes suggest evaluations must test diverse misalignment patterns. Nevertheless, we discovered other more pronounced failure modes (than power-seeking) such as specification gaming and resistance to goal modification.
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