Penny for Your Prompts: A Measurement Study of Contextual Leakage in AI Chat Platforms
Measurement study finds AI chatbot platforms leak user context through web-app-layer mechanisms even when model-level safeguards hold—a GDPR concern for enterprises permitting employee use of third-party chat tools.
Summary written by editorial AI · Source link below
arXiv:2610.37467v1 Announce Type: new Abstract: 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
Editorial Analysis
Employees sharing sensitive data with AI chatbots face exposure not just through model behaviour but through platform web-application vulnerabilities.
Update your acceptable-use policy for third-party AI chatbots and assess platform-level data-leakage risks beyond model-layer privacy controls.
AI chatbot platforms can leak employee-shared data through web-app flaws—review vendor agreements and usage policies.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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