构建真实办公场景的智能体评估基准，测试其效率与安全性。
ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces
- 用五个高保真模拟服务构建可复现的工作流环境。
- 全量配置下任务成功率39%-64%，但存在7%-33%不安全操作。
- 发现八类安全隐患，适合安全与评估方向研究者参考。
大型语言模型（LLM）代理正被用于自动化邮件、日程安排、文档管理等生产力任务，但在真实服务上评估存在不可逆风险。现有基准依赖简化环境，难以捕捉真实的多服务、状态化工作流。我们提出 ClawsBench，一个面向真实生产力场景的 LLM 代理评估基准。包含五个高保真模拟服务（Gmail、Slack、Google Calendar、Google Docs、Google Drive），支持完整状态管理与确定性快照/恢复，并设计了44个结构化任务，涵盖单服务、跨服务及安全敏感场景。我们将代理架构解耦为两个独立控制维度：领域技能（通过渐进披露注入API知识）和元提示（协调跨服务行为），并系统性地测试二者独立与联合影响。在6个模型、4种代理框架、33种条件下实验表明，全量配置下任务成功率达39%-64%，但不安全操作率高达7%-33%。在 OpenClaw 上，表现最佳的五个模型任务成功率在53%-63%之间，不安全操作率7%-23%，两指标无一致排序。我们识别出八类典型不安全行为模式，包括多步沙箱提升和静默合同修改。数据轨迹与未来数据集已公开于 https://clawsbench.com。
原文摘要 · Abstract (English)
Large language model (LLM) agents are increasingly deployed to automate productivity tasks (e.g., email, scheduling, document management), but evaluating them on live services is risky due to potentially irreversible changes. Existing benchmarks rely on simplified environments and fail to capture realistic, stateful, multi-service workflows. We introduce ClawsBench, a benchmark for evaluating and improving LLM agents in realistic productivity settings. It includes five high-fidelity mock services (Gmail, Slack, Google Calendar, Google Docs, Google Drive) with full state management and deterministic snapshot/restore, along with 44 structured tasks covering single-service, cross-service, and safety-critical scenarios. We decompose agent scaffolding into two independent levers (domain skills that inject API knowledge via progressive disclosure, and a meta prompt that coordinates behavior across services) and vary both to measure their separate and combined effects. Experiments across 6 models, 4 agent harnesses, and 33 conditions show that with full scaffolding, agents achieve task success rates of 39-64% but exhibit unsafe action rates of 7-33%. On OpenClaw, the top five models fall within a 10 percentage-point band on task success (53-63%), with unsafe action rates from 7% to 23% and no consistent ordering between the two metrics. We identify eight recurring patterns of unsafe behavior, including multi-step sandbox escalation and silent contract modification. We release the trajectories and future dataset at https://clawsbench.com.
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