{"slug": "a-measurement-study-of-contextual-leakage-in-ai-chat-platforms", "title": "A Measurement Study of Contextual Leakage in AI Chat Platforms", "summary": "A measurement study submitted to arXiv on 27 Oct 2026 audited the web interfaces of 42 general-purpose and domain-specific AI chatbot platforms (health, legal and education) and found that 21 leak conversation titles to third-party trackers while nine leak verbatim user prompts. The researchers instrumented the browser at the JavaScript engine level to attribute each data flow to the exact user action that triggered it and the DOM capture mechanism used to retrieve its content, a granularity the authors say is invisible to network-layer analysis alone. The study also found a substantial number of chatbot platforms sending user emails to third-party trackers, enabling conversational context to be tied to a user's identity.", "body_md": "# Computer Science > Cryptography and Security\n\n  [Submitted on 27 Oct 2026]\n\n# Title:Penny for Your Prompts: A Measurement Study of Contextual Leakage in AI Chat Platforms\n\n[View PDF](https://arxiv.org/pdf/2610.37467)\n\n[HTML (experimental)](https://arxiv.org/html/2610.37467v1)\n\nAbstract:AI chatbot platforms have become one of the most popular web interfaces users interact with online, routinely receiving personal and intimate information shared under an expectation of privacy. Yet, on the web application side, these platforms have the ability to embed third-party analytics, advertising, and session replay scripts that operate silently alongside the chatbot, creating an underexamined channel for conversational data leakage towards third parties. We conduct the first systematic large-scale measurement study of contextual leakage in AI chatbots, auditing the web interfaces of 42 general-purpose and domain-specific (health, legal, and education) platforms. Beyond standard network interception, we instrument the browser at the JavaScript engine level to attribute each data flow to the exact user action that triggered it, and the DOM capture mechanism used to retrieve its content, a granularity invisible to network-layer analysis alone. We find that 21 of these platforms leak conversation titles to third-party trackers, while nine leak verbatim user prompts. Beyond content, we observe a substantial amount of chatbot platforms sending user emails to third-party trackers, enabling the association of conversational context with a user's identity. Our findings cast traditional web privacy issues under a new light, highlighting the privacy issues that are inherent in how users interact with modern AI chatbots.\n    \n\n### Additional Features\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/a-measurement-study-of-contextual-leakage-in-ai-chat-platforms", "canonical_source": "https://arxiv.org/abs/2610.37467", "published_at": "2026-10-28 12:15:16+00:00", "updated_at": "2026-10-28 12:42:35.513400+00:00", "lang": "en", "topics": ["ai-safety", "ai-ethics", "artificial-intelligence"], "entities": ["arXiv", "Penny for Your Prompts"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/a-measurement-study-of-contextual-leakage-in-ai-chat-platforms", "markdown": "https://wpnews.pro/news/a-measurement-study-of-contextual-leakage-in-ai-chat-platforms.md", "text": "https://wpnews.pro/news/a-measurement-study-of-contextual-leakage-in-ai-chat-platforms.txt", "jsonld": "https://wpnews.pro/news/a-measurement-study-of-contextual-leakage-in-ai-chat-platforms.jsonld"}}