构建真实生产环境下的AI代理控制测试平台，评估攻防能力。
LinuxArena: A Control Setting for AI Agents in Live Production Software Environments
- 在真实多服务环境中运行代理，模拟软件工程任务与安全攻击
- 23%攻击成功率未被监测模型发现，验证平台有效性
- 适合研究AI安全、攻防对抗及监控机制的开发者
我们提出LinuxArena，一个让代理直接在实时多服务生产环境中运行的控制设定。该平台包含20个环境、1,671个主任务（代表合法软件工程工作）和184个侧任务（如数据外泄、植入后门等安全失败场景），是目前规模最大、最多样化的软件工程控制测试平台。通过破坏性评估（测量攻击者在完成主任务时执行侧任务的成功率）和监控评估（衡量监控模型检测破坏行为的能力）验证其有效性。在1%步进误报率下，使用GPT-5-nano作为可信监控器时，Claude Opus 4.6实现约23%的未被察觉的破坏成功率。此外，我们发布了LaStraj数据集，包含人工设计的攻击轨迹，其绕过监控的能力显著优于模型生成的攻击，表明当前攻击策略尚未饱和LinuxArena。结果表明，该平台对攻防双方均具有足够挑战性，是未来控制协议开发与评估的理想实验场。
原文摘要 · Abstract (English)
We introduce LinuxArena, a control setting in which agents operate directly on live, multi-service production environments. LinuxArena contains 20 environments, 1,671 main tasks representing legitimate software engineering work, and 184 side tasks representing safety failures such as data exfiltration and backdooring, making it the largest and most diverse control setting for software engineering to date. We validate LinuxArena is useful for control research by running sabotage evaluations, which measure whether attackers can complete side tasks while working on main tasks, and monitor evaluations, which measure a monitor model's ability to detect sabotage attempts. Against a GPT-5-nano trusted monitor at a 1\% step-wise false positive rate, Claude Opus 4.6 achieves roughly a 23% undetected sabotage success rate. We additionally release LaStraj, a dataset of human-crafted attack trajectories that evade monitors at substantially higher rates than any model-generated attacks we elicited, showing that current attack policies do not saturate LinuxArena. These results suggest that LinuxArena has meaningful headroom for both attackers and defenders, making it a strong testbed for developing and evaluating future control protocols.
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