用大模型代理自动检测数据经纪商网站的暗黑设计，发现可行但有局限。
On the Suitability of LLM-Driven Agents for Dark Pattern Audits
- 构建可端到端操作的智能代理，自动完成用户数据请求流程。
- 在456个网站上识别出多种暗黑模式，分类结果具备一定可靠性。
- 适合关注隐私保护与自动化审计的研究者或从业者参考。
随着大模型驱动的智能体开始自主浏览网页，其解读和应对操纵性界面设计的能力变得至关重要。一个核心问题是：这些智能体能否可靠识别界面设计中的摩擦、误导和强迫等暗黑模式？我们聚焦于具有实际影响的场景——与加州消费者隐私法案（CCPA）相关数据权利请求提交相关的网站门户。这些门户虽用于实现法定权利，但其交互式设计可能被用于便利、加重负担或隐晦阻碍用户行使权利。为此，我们设计并部署了一个大模型驱动的审计智能体，能够端到端遍历权利请求流程，结构化收集证据，并对潜在暗黑模式进行分类。我们在456个数据经纪商网站上评估了该智能体的三项能力：（1）持续定位并完成请求流程的能力；（2）暗黑模式分类的可靠性与可复现性；（3）其失效或判断失误的条件。研究结果揭示了使用大模型智能体进行大规模暗黑模式审计的可行性与局限性。
原文摘要 · Abstract (English)
As LLM-driven agents begin to autonomously navigate the web, their ability to interpret and respond to manipulative interface design becomes critical. A fundamental question that emerges is: can such agents reliably recognize patterns of friction, misdirection, and coercion in interface design (i.e., dark patterns)? We study this question in a setting where the workflows are consequential: website portals associated with the submission of CCPA-related data rights requests. These portals operationalize statutory rights, but they are implemented as interactive interfaces whose design can be structured to facilitate, burden, or subtly discourage the exercise of those rights. We design and deploy an LLM-driven auditing agent capable of end-to-end traversal of rights-request workflows, structured evidence gathering, and classification of potential dark patterns. Across a set of 456 data broker websites, we evaluate: (1) the ability of the agent to consistently locate and complete request flows, (2) the reliability and reproducibility of its dark pattern classifications, and (3) the conditions under which it fails or produces poor judgments. Our findings characterize both the feasibility and the limitations of using LLM-driven agents for scalable dark pattern auditing.
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