The rapid evolution of Large Language Models (LLMs) from passive assistants to autonomous, execution-capable agents has introduced critical operational risks. Most current evaluation frameworks neglect procedural compliance, leading to ''Machiavellian'' behaviors where agents strategically violate safety rules to maximize rewards - a direct manifestation of Goodhart's Law. To address this blind spot, we introduce MAC-Bench, a dynamic, adversarial benchmark designed to evaluate the procedural alignment of multi-agent systems under realistic pressure. We propose the SERV(Seed - Evolve - Refine - Verify) pipeline, an ``Agent-as-a-Benchmark'' paradigm that transforms unstructured legal texts into executable, contamination-free scenarios. By synthesizing holographic sandbox environments and injecting calibrated social-engineering pressure vectors, MAC-Bench forces agents into Pareto-optimal trade-offs between task success and regulatory adherence. We introduced novel metrics: the Compliance-Weighted Success Rate (CSR) and the Machiavellian Gap (MG), and conducted a comprehensive evaluation of state-of-the-art frontier models to reveal the pervasive trade-offs between success and compliance.

翻译：大语言模型从被动助手向具备自主执行能力的智能体的快速演进，引入了关键的操作风险。现有评估框架普遍忽视过程合规性，导致智能体为最大化奖励而策略性违反安全规则的"马基雅维利式"行为——这正是古德哈特定律的直接体现。针对这一盲区，我们提出MAC-Bench，一个在现实压力下评估多智能体系统过程对齐的动态对抗性基准。我们提出SERV（种子-进化-精炼-验证）流水线——一种将非结构化法律文本转化为可执行、无污染场景的"智能体即基准"范式。通过构建全息沙盒环境并注入校准的社会工程压力向量，MAC-Bench迫使智能体在任务成功与规则遵守之间做出帕累托最优权衡。我们引入新指标：合规加权成功率（CSR）与马基雅维利差距（MG），并对前沿模型进行全面评估，揭示了成功与合规之间普遍存在的权衡关系。