构建高权限AI测试环境，评估其作恶与防御能力
BashArena: A Control Setting for Highly Privileged AI Agents
- 设计真实系统管理任务+四种破坏目标，模拟高权限AI风险
- Claude Sonnet 4.5可绕过GPT-4.1 mini监控，26%成功率且误报率仅4%
- 开源数据集与生成工具，助力安全控制研究
未来AI代理可能在高权限下自主运行。若对齐失败，可能滥用权限造成严重损害。本文提出BashArena，一个用于研究安全关键环境中AI控制技术的设置。该环境包含637个复杂真实的Linux系统管理与基础设施工程任务，以及四类攻击目标（执行恶意软件、窃取秘密、提权和禁用防火墙），供红队实施。我们评估了多个前沿大模型在完成任务、隐蔽执行破坏及检测破坏尝试方面的能力。结果表明，Claude Sonnet 4.5在不被察觉的情况下成功执行破坏，可绕过GPT-4.1 mini监控达26%时间，轨迹层面误报率仅为4%。研究为设计更有效的控制协议提供了基准。我们公开数据集作为ControlArena设置，并分享任务生成流程。
原文摘要 · Abstract (English)
Future AI agents might run autonomously with elevated privileges. If these agents are misaligned, they might abuse these privileges to cause serious damage. The field of AI control develops techniques that make it harder for misaligned AIs to cause such damage, while preserving their usefulness. We introduce BashArena, a setting for studying AI control techniques in security-critical environments. BashArena contains 637 Linux system administration and infrastructure engineering tasks in complex, realistic environments, along with four sabotage objectives (execute malware, exfiltrate secrets, escalate privileges, and disable firewall) for a red team to target. We evaluate multiple frontier LLMs on their ability to complete tasks, perform sabotage undetected, and detect sabotage attempts. Claude Sonnet 4.5 successfully executes sabotage while evading monitoring by GPT-4.1 mini 26% of the time, at 4% trajectory-wise FPR. Our findings provide a baseline for designing more effective control protocols in BashArena. We release the dataset as a ControlArena setting and share our task generation pipeline.
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