BARC-Bench: Benign Asks, Risky Consequences

arXiv cs.AI Papers

Summary

BARC-Bench 引入了 110 个任务、覆盖九个模拟工作区环境的基准,用于评估 AI 智能体在隐含安全边界下的行为;结果显示前沿模型配置的安全完成率仅在 9.1% 到 72.7% 之间,表明自主委托仍存在显著的不对称操作风险。

arXiv:2610.32024v1 Announce Type: new Abstract: Task instructions from users for agents are inherently underspecified, especially regarding safety boundaries--creating behavioral risks. To study these risks, we introduce BARC-Bench, a collection of 110 tasks across nine simulated workspace environments where benign requests leave openings for unsafe actions. In this benchmark, we evaluate agent behavior against safety boundaries established by workspace records, implicit environmental context, and unstated social norms. For example, an agent asked to schedule off-boarding discussions must arrange the meeting without prematurely revealing an employee's resignation through a shared calendar invitation. Evaluating four frontier model configurations across five attempts per task reveals that models are not consistently safe: the share of tasks completed safely on all five attempts ranges from 9.1% to 72.7%, with even the strongest configuration producing a safety violation on 27.3% of tasks. These results highlight an asymmetric operational risk: while models frequently complete complex workflows, their inability to consistently respect unstated boundaries presents a substantial barrier to safe autonomous delegation.
Original Article

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