hiw3c 日报精选
arXiv cs.AI/CL/LG 网页快照

EpiSEM-Bench:在信息缺失的情况下,评估大型语言模型代理的安全性和证据恢复能力

原文标题 · EpiSEM-Bench: Evaluating Safety and Evidence Recovery in LLM Agents under Missing Evidence

作者:Qijin Zhang, Hanzhen Wang, Yuehan Zhao | 使用工具的语言模型代理依靠环境信息来决定是否允许某种行为。当工具响应中遗漏了与安全性相关的信息时,即使用户的请求是合法的,且返回的每条信息都是准确的,代理仍然可能会执行被禁止的行为。我们提出了“bench”这一可执行的基准测试,用于评估不安全的执行行为、安全性边界以及证据恢复等方面的问题。

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Artificial Intelligence

Title: EpiSEM-Bench: Evaluating Safety and Evidence Recovery in LLM Agents under Missing Evidence

Abstract: Tool-using language model agents rely on environmental facts to decide whether an action is permitted. When a tool response omits a safety-relevant fact, an agent may carry out a prohibited action even though the user request is benign and every returned statement is true. We introduce \bench{}, an executable benchmark for evaluating unsafe execution, safety boundaries, and evidence recovery in LLM agents under missing evidence. It contains 300 matched unsafe/safe task pairs across six domains. By systematically suppressing critical facts, the benchmark measures unsafe execution under critical evidence omission and determines the minimum observed failure cardinality, including failures with full evidence and cases with no observed failure after exhaustive testing. An independent audit tool tests whether agents seek missing facts and then withhold prohibited actions while completing permissible tasks. The benchmark reveals substantial variation across the ten models in omission robustness, evidence acquisition, and appropriate action. Resistance to missing evidence does not necessarily translate into useful recovery. Recovery access reduces unsafe execution for all ten models, but agents differ in whether they actively obtain the missing facts and use them to act appropriately in both worlds.

Submission history

Access Paper:

Additional Features

Current browse context:

References & Citations

BibTeX formatted citation

Bookmark

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .