DPAgent-in-the-Middle: Agentic Defense and Repair Against AI-Groomed Deceptive Patterns

Computer Science > Cryptography and Security [Submitted on 5 Jun 2026] DPAgent-in-the-Middle: Agentic Defense and Repair Against AI-Groomed Deceptive Patterns Zewei Shi, Ruoxi Sun, Haoyang Li, Seong Oun Hwang, Feng Liu, Minhui Xue, Xingliang Yuan Privacy deceptive patterns in web interfaces systematically manipulate users into disclosing personal data, yet existing defenses are fragmented, static, and increasingly vulnerable to manipulation by large language models. Moreover, data voids, areas of information scarcity within the web ecosystem, create fertile ground for adversaries to inject misleading content that can be scraped and learned by AI systems, thereby amplifying both deceptive design and model misbehavior. In this paper, we formalize a new threat model, AI grooming, where attackers exploit data voids to seed benign-looking but malicious samples that corrupt model reasoning and normalize deceptive practices. To address this threat in privacy deceptive patterns, we present DPAgent, an agentic and reasoning-aware framework that orchestrates four specialized agents to mitigate the AI Grooming threat via a proactive defense that combines latent space purification with defensive prompting and operates directly in live web environments to proactively explore, detect, and repair privacy deceptive user interfaces before they reach end users. Extensive evaluations show that DPAgent detects 90.98% of groomed samples, achieves state-of-the-art privacy deceptive pattern detection with a micro F1 of 0.816, explores over 80% of pattern types while visiting only about 10% of the pages required by baselines, and successfully repairs 77% of detected deceptive interfaces. A large-scale study of 485 websites in the wild reveals that up to 98% contain at least one privacy deceptive pattern, over 90% of which can be mitigated by DPAgent. User studies further confirm that DPAgent effectively reduces privacy risks while preserving browsing experience. Our results demonstrate the promise of agent-in-the-middle defenses for securing the web UI supply chain against deceptive design and emerging AI threats rooted in data void exploitation. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.06914 [cs.CR]   (or arXiv:2606.06914v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.06914 Focus to learn more Submission history From: Zewei Shi [view email] [v1] Fri, 5 Jun 2026 05:26:42 UTC (6,440 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)