DarkBench: Benchmarking Dark Patterns in Large Language Models

• URL: http://arxiv.org/abs/2503.10728v1
• Date: Thu, 13 Mar 2025 11:48:42 GMT
• Title: DarkBench: Benchmarking Dark Patterns in Large Language Models
• Authors: Esben Kran, Hieu Minh "Jord" Nguyen, Akash Kundu, Sami Jawhar, Jinsuk Park, Mateusz Maria Jurewicz,
• Abstract summary: We introduce DarkBench, a benchmark for detecting dark design patterns in large language models (LLMs)<n>Our benchmark comprises 660 prompts across six categories: brand bias, user retention, sycophancy, anthropomorphism, harmful generation, and sneaking.
• Score: 0.6597195879147557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce DarkBench, a comprehensive benchmark for detecting dark design patterns--manipulative techniques that influence user behavior--in interactions with large language models (LLMs). Our benchmark comprises 660 prompts across six categories: brand bias, user retention, sycophancy, anthropomorphism, harmful generation, and sneaking. We evaluate models from five leading companies (OpenAI, Anthropic, Meta, Mistral, Google) and find that some LLMs are explicitly designed to favor their developers' products and exhibit untruthful communication, among other manipulative behaviors. Companies developing LLMs should recognize and mitigate the impact of dark design patterns to promote more ethical AI.

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