🤖 AI Summary
This work addresses the pervasive risk of inadvertent personal identifiable information (PII) leakage by ordinary users during interactions with cloud-based large language models, stemming from a lack of effective control over their sensitive data. To mitigate this, we propose a lightweight browser overlay tool that, for the first time, adapts enterprise-grade PII redaction techniques into a consumer-oriented, client-side solution. The system integrates front-end entity recognition with anonymization mechanisms and introduces an innovative “smokescreen” behavior—generating deceptive interaction patterns to actively disrupt third-party user profiling. Fully open-source and extensible, our approach preserves the usability of AI interactions while significantly enhancing users’ autonomy over their PII, thereby offering robust protection against web-based privacy breaches.
📝 Abstract
AI chatbots have quietly become the world's most popular therapists, coaches, and confidants. Users of cloud-based LLM services are increasingly shifting from simple queries like idea generation and poem writing, to deeply personal interactions. As Large Language Models increasingly assume the role of our confessors, we are witnessing a massive, unregulated transfer of sensitive personal identifiable information (PII) to powerful tech companies with opaque privacy practices. While the enterprise sector has made great strides in addressing data leakage concerns through sophisticated guardrails and PII redaction pipelines, these powerful tools have functionally remained inaccessible for the average user due to their technical complexity. This results in a dangerous trade off for individual users. In order to receive the therapeutic or productivity benefits of AI, users need to abandon any agency they might otherwise have over their data, often without a clear mental model of what is being shared, and how it might be used for advertising later on. This work addresses this interaction gap, applying the redaction pipelines of enterprise-grade redaction into an intuitive, first-of-its-kind, consumer-facing, and free experience. Specifically, this work introduces a scalable, browser-based intervention designed to help align user behavior with their privacy preferences during web-based AI interactions. Our system introduces two key mechanisms: local entity anonymization to prevent data leakage, and 'smokescreens': autonomous agent activity to disrupt third-party profiling. An open-source implementation is accessible at the GitHub repository below.