arXiv cs.AI· Kangkang Li, Kelvin Luu, Lily Ma, Tu Trinh, Ying Liu, Mohamed Elfeki·· 12 小时前AI 评分52
BARC-Bench：看似无害的请求，暗藏风险的后果
BARC-Bench: Benign Asks, Risky Consequences
AI 导读
研究者提出 BARC-Bench，包含 9 个模拟工作环境中的 110 个任务，这些任务表面无害，却为不安全操作留下空间，用于评估智能体是否遵守由工作区记录、隐含环境上下文和未明说社会规范构成的安全边界。
正文 · 原文
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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32024 [cs.AI] |
| (or arXiv:2610.32024v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32024 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tu Trinh [view email]
[v1]
Fri, 23 Oct 2026 00:53:24 UTC (152 KB)
来源：arXiv cs.AI · arxiv.org