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.

翻译：随着LLM驱动的智能体开始自主浏览网络，其解释和响应操纵性界面设计的能力变得至关重要。一个根本性问题随之浮现：此类智能体能否可靠识别界面设计中的摩擦、误导和胁迫模式（即暗黑模式）？我们在工作流程具有实际后果的场景中研究该问题：与提交CCPA相关数据权利请求相关的网站门户。这些门户将法定权利操作化，但其实现为交互式界面，其设计可被结构化以促进、加重或微妙地阻碍这些权利的行使。我们设计并部署了一个LLM驱动的审计智能体，能够端到端遍历权利请求工作流，进行结构化证据收集，并对潜在暗黑模式进行分类。在456个数据经纪网站集合中，我们评估：（1）智能体持续定位并完成请求流程的能力；（2）其暗黑模式分类的可靠性与可复现性；（3）其失效或产生错误判断的条件。我们的研究结果既揭示了使用LLM驱动智能体进行可扩展暗黑模式审计的可行性，也明确了其局限性。