# PrivWeb: Unobtrusive and Content-aware Privacy Protection For Web Agents

arXiv: https://arxiv.org/abs/2509.11939 · HTML: https://mlsanity.com/paper/2509.11939/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2025.

## Built on by (most cited first, 9 of 9)

- [Comparing Human Oversight Strategies for Computer-Use Agents](https://mlsanity.com/paper/2604.04918/) (2026)
- [Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security](https://mlsanity.com/paper/2605.23989/) (2026)
- [Clawed and Dangerous: Can We Trust Open Agentic Systems?](https://mlsanity.com/paper/2603.26221/) (2026)
- [Minim: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization](https://mlsanity.com/paper/2606.13949/) (2026)
- [MalURLBench: A Benchmark Evaluating Agents' Vulnerabilities When Processing Web URLs](https://mlsanity.com/paper/2601.18113/) (2026)
- [Focused on the User, Overlooking the Risks: Security and Privacy Understandings, Practices and Challenges of Independent Chinese AI Agent Developers](https://mlsanity.com/paper/2606.03190/) (2026)
- [PriMobiBench: Characterizing Visual Privacy Leakage in VLM-Driven Mobile GUI Agents](https://mlsanity.com/paper/2609.13873/) (2026)
- [PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents](https://mlsanity.com/paper/2609.35937/) (2026)
- [PrivacyGuard: Source-Linked Sanitization and Locally Resolvable PII References for Cloud GUI Agents](https://mlsanity.com/paper/2610.16778/) (2026)

## Abstract (arXiv)

While web agents gained popularity by automating web interactions, their requirement for interface access introduces significant privacy risks that are understudied, particularly from users' perspective. Through a formative study (N=15), we found users frequently misunderstand agents' data practices, and desired unobtrusive, transparent data management. To achieve this, we designed and implemented PrivWeb, a trusted add-on on web agents that utilizes a localized LLM to anonymize private information on interfaces according to user preferences. It features privacy categorization schema and adaptive notifications that selectively pauses tasks for user control over information collection for highly sensitive information, while offering non-disruptive options for less sensitive information, minimizing human oversight. The user study (N=14) across travel, information retrieval, shopping, and entertainment tasks compared PrivWeb with baselines without notification and without control for private information access, where PrivWeb reduced perceived privacy risks with no associated increase in cognitive effort, and resulted in higher overall satisfaction.