U.S. AI security policy is increasingly shaped by an $\textit{LLM Mirage}$, the belief that national security risks scale in proportion to the compute used to train frontier language models. That premise fails in two ways. It miscalibrates strategy because adversaries can obtain weaponizable capabilities with task-specific systems that use specialized data, algorithmic efficiency, and widely available hardware, while compute controls harden only a high-end perimeter. It also destabilizes regulation because, absent a settled definition of "AI weaponization," compute thresholds are easily renegotiated as domestic priorities shift, turning security policy into a proxy contest over industrial competitiveness. We analyze how the LLM Mirage took hold, propose an intent-and-capability definition of AI weaponization grounded in effects and international humanitarian law, and outline measurement infrastructure based on live benchmarks across the full AI Triad (data, algorithms, compute) for weaponization-relevant capabilities.

翻译：美国人工智能安全政策日益受到一种"大型语言模型幻象"的影响，即认为国家安全风险与训练前沿语言模型所消耗的计算资源成正比。这一前提在两方面存在缺陷。首先，它导致战略误判——对手可通过使用专用数据、算法优化及广泛可得硬件构建的任务专用系统获得可武器化能力，而计算控制仅能强化高端防御边界。其次，它破坏监管稳定性——在缺乏"人工智能武器化"明确定义的情况下，计算阈值会随着国内优先事项的变化而被重新协商，使安全政策沦为产业竞争力的代理战场。本文分析了大型语言模型幻象的形成机制，提出基于实际效果与国际人道法的意图-能力双维武器化定义，并构建覆盖人工智能三元要素（数据、算法、计算）的动态基准测试体系，以评估武器化相关能力。