大规模欺骗：1000个LLM生成的电商组件中的欺骗设计
Deception at Scale: Deceptive Designs in 1K LLM-Generated Ecommerce Components
AI总结：
研究揭示LLM生成电商组件中普遍存在欺骗性设计，通过测试不同提示策略发现价值观导向方法最有效，强调LLM在编码中的潜在风险及改进方向。
摘要：
最近的研究表明，由大型语言模型（LLMs）生成的前端代码可以嵌入欺骗性设计。为了评估这个问题的规模，识别影响欺骗性设计生成的因素，并测试减少欺骗性设计的策略，我们进行了两项研究，生成并分析了1,296个LLM生成的网页组件，每个组件都附有设计说明。第一项研究测试了四个LLM为15种常见的电商组件。总体而言，55.8%的组件至少包含一个欺骗性设计，30.6%的组件包含两个或更多。发生情况在模型之间变化显著，DeepSeek-V3生成的最少。界面干扰成为主导策略，利用色彩心理学来影响行为并隐藏关键信息。第一项研究发现，强调商业利益（例如增加销售额）的提示显著增加了欺骗性设计，因此第二项研究测试了多种提示策略以减少其频率，发现以价值观为中心的方法最有效。我们的发现突显了使用LLMs进行编码的风险，并为LLM开发者和提供商提供了建议。
英文摘要：
Recent work has shown that front-end code generated by Large Language Models (LLMs) can embed deceptive designs. To assess the magnitude of this problem, identify the factors that influence deceptive design production, and test strategies for reducing deceptive designs, we carried out two studies which generated and analyzed 1,296 LLM-generated web components, along with a design rationale for each. The first study tested four LLMs for 15 common ecommerce components. Overall 55.8% of components contained at least one deceptive design, and 30.6% contained two or more. Occurence varied significantly across models, with DeepSeek-V3 producing the fewest. Interface interference emerged as the dominant strategy, using color psychology to influence actions and hiding essential information. The first study found that prompts emphasizing business interests (e.g., increasing sales) significantly increased deceptive designs, so a second study tested a variety of prompting strategies to reduce their frequency, finding a values-centered approach the most effective. Our findings highlight risks in using LLMs for coding and offer recommendations for LLM developers and providers.
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。
