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arXiv 2604.06367cs.CRcs.AIcs.LG
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WebSP-Eval:在网站安全与隐私任务上评估网络代理

WebSP-Eval: Evaluating Web Agents on Website Security and Privacy Tasks

University of Wisconsin-Madison(威斯康星大学麦迪逊分校)

Guruprasad Viswanathan Ramesh, Asmit Nayak, Basieem Siddique, Kassem Fawaz

AI总结:

提出WebSP-Eval框架,通过200个任务实例和自动化评估器,测试多模态大模型在网站安全与隐私任务上的表现,发现状态UI元素(如开关)导致超过45%的任务失败。

摘要:

网络代理自动化浏览器任务,从简单的表单填写到复杂的工作流程(如订购杂货)。虽然当前的基准测试评估通用性能(如WebArena)或针对恶意行为的安全性(如SafeArena),但没有现有框架评估代理成功执行面向用户的网站安全和隐私任务的能力,例如管理cookie偏好、配置隐私敏感账户设置或撤销非活动会话。为填补这一空白,我们引入了WebSP-Eval,一个用于衡量网络代理在网站安全和隐私任务上性能的评估框架。WebSP-Eval包括:1)一个手动制作的任务数据集,涵盖28个网站的200个任务实例;2)一个强大的代理系统,支持使用自定义Google Chrome扩展在多次运行中进行账户和初始状态管理;以及3)一个自动化评估器。我们使用最先进的多模态大语言模型评估了总共8个网络代理实例,对网站、任务类别和UI元素进行了细粒度分析。我们的评估显示,当前模型在可靠解决网站安全和隐私任务方面自主探索能力有限,并且在特定任务类别和网站上表现困难。关键的是,我们发现状态UI元素是代理失败的主要原因,其中开关导致许多模型超过45%的任务失败。

英文摘要:

Web agents automate browser tasks, ranging from simple form completion to complex workflows like ordering groceries. While current benchmarks evaluate general-purpose performance~(e.g., WebArena) or safety against malicious actions~(e.g., SafeArena), no existing framework assesses an agent's ability to successfully execute user-facing website security and privacy tasks, such as managing cookie preferences, configuring privacy-sensitive account settings, or revoking inactive sessions. To address this gap, we introduce WebSP-Eval, an evaluation framework for measuring web agent performance on website security and privacy tasks. WebSP-Eval comprises 1) a manually crafted task dataset of 200 task instances across 28 websites; 2) a robust agentic system supporting account and initial state management across runs using a custom Google Chrome extension; and 3) an automated evaluator. We evaluate a total of 8 web agent instantiations using state-of-the-art multimodal large language models, conducting a fine-grained analysis across websites, task categories, and UI elements. Our evaluation reveals that current models suffer from limited autonomous exploration capabilities to reliably solve website security and privacy tasks, and struggle with specific task categories and websites. Crucially, we identify stateful UI elements are a primary reason for agent failure, with toggles causing more than 45% task failure across many models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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