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Cited by 7Ethics and Social Impacts of AIInnovative Human-Technology InteractionSoftware Engineering Research

Deception at Scale: Deceptive Designs in 1K LLM-Generated E-Commerce Components

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Chen, Ziwei, Shen, Jiawen, Luna, Zhang, Hanyu, Vaccaro, Kristen·2026·Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3772318.3791063
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Abstract

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.

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  • Even LLM-generated software has tendencies to embed dark patterns, particularly when prompted to optimize for business goals and product success.

    “Furthermore, even LLM-generated software has tendencies to embed dark patterns, particularly when prompted to optimize for business goals and product success (Chen et al., 2026;Krauß et al., 2025).”

    From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Chen et al., 2026

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Cited by

Sorted by citations

Authors
Chen, Z., Shen, J., Luna, Zhang, H., & Vaccaro, K.
Affiliations
University of California San Diego (US)
ORCID
Venue
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2026-04-13
Pages
1-18
DOI
10.1145/3772318.3791063
OpenAlex
W4407764297
Cited by
7
References
2
Citations by year
2026: 5 · 2025: 2
FWCI
23.53
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
Keywords
dark patterns, large language models, LLM-generated code, color psychology, prompting strategies, DeepSeek-V3, front-end web development, user interface design

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