# 50 Shades of Deceptive Patterns: A Unified Taxonomy, Multimodal Detection, and Security Implications

arXiv: https://arxiv.org/abs/2501.13351 · HTML: https://mlsanity.com/paper/2501.13351/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [SpatialJB: How Text Distribution Art Becomes the "Jailbreak Key" for LLM Guardrails](https://mlsanity.com/paper/2601.09321/) (2026)
- [Don't Click That: Teaching Web Agents to Resist Deceptive Interfaces](https://mlsanity.com/paper/2605.09497/) (2026)
- [On the Suitability of LLM-Driven Agents for Dark Pattern Audits](https://mlsanity.com/paper/2603.03881/) (2026)

## Abstract (arXiv)

Deceptive patterns (DPs) are user interface designs deliberately crafted to manipulate users into unintended decisions, often by exploiting cognitive biases for the benefit of companies or services. While numerous studies have explored ways to identify these deceptive patterns, many existing solutions require significant human intervention and struggle to keep pace with the evolving nature of deceptive designs. To address these challenges, we expanded the deceptive pattern taxonomy from security and privacy perspectives, refining its categories and scope. We created a comprehensive dataset of deceptive patterns by integrating existing small-scale datasets with new samples, resulting in 6,725 images and 10,421 DP instances from mobile apps and websites. We then developed DPGuard, a novel automatic tool leveraging commercial multimodal large language models (MLLMs) for deceptive pattern detection. Experimental results show that DPGuard outperforms state-of-the-art methods. Finally, we conducted an extensive empirical evaluation on 2,000 popular mobile apps and websites, revealing that 23.61% of mobile screenshots and 47.27% of website screenshots feature at least one deceptive pattern instance. Through four unexplored case studies that inform security implications, we highlight the critical importance of the unified taxonomy in addressing the growing challenges of Internet deception.