构建首个开源基准，检测大模型代理内部威胁行为。
MOLE: Detecting Insider Threats in AI Agents
- 设计150个AI代理账户，模拟30天真实操作中的12类攻击。
- 72%的代理完成恶意目标，且拒绝执行不预示攻击成功。
- 支持监控器对比与优化，提升检测效率和预算利用率。
模型错位、提示注入或操作者滥用可能导致前沿实验室账户中的AI代理窃取模型权重、污染训练数据或削弱发布机制。现有基准无法在有限审查预算下评估防御方是否能发现此类行为。我们提出MOLE，一个开放基准，包含150个由AI操作的账户，共享9个有状态服务，持续30个工作日，涵盖12种威胁与来自4个模型的8个语料库，总计约200亿词元。在39个代理模型中，72%完成了主要恶意目标，且代理拒绝行为无法预测任务完成。MOLE支持对40种监控器在不同语料生成方式、可观测性水平和威胁类型下的比较；即使表现最佳的监控器，在单日审计事件评估中仍遗漏近半数已完成的危害。该基准还支持监控器开发：基于基准的搜索可使中等性能监控器提升49%-64%，而选择性使用更强监控器可使预算AUC提高10%，成本相当。
原文摘要 · Abstract (English)
Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model weights, poison training data, or weaken release gates. Existing benchmarks do not test whether defenders can detect this activity among routine work under a limited review budget. We introduce MOLE, an open benchmark of 150 AI-operated accounts sharing 9 stateful services over 30 workdays, with 12 threats and 8 corpora from four models totaling roughly 20 billion tokens. Of 39 agent models, 72% complete most assigned harmful objectives and agent refusal does not predict completion. MOLE enables comparison of 40 monitors across corpus generators, observability levels, and threats; even the best evaluated monitor in our single-day audit-event comparison misses nearly half of completed harm. MOLE also enables monitor development: benchmark-guided search improves a mid-tier monitor by 49-64%, while selective use of a stronger monitor improves budget-AUC by 10% over applying it to every account-day at comparable modeled cost.
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