A Measurement Study of Contextual Leakage in AI Chat Platforms

This paper presents 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 using JavaScript engine-level instrumentation to attribute data flows to specific user actions. The authors find that 21 platforms leak conversation titles to third-party trackers and nine leak verbatim user prompts, while many also send user emails to third-party trackers, enabling association of conversational context with user identity. The work is to appear in the IEEE Symposium on Security & Privacy (S&P), May 2027 (arXiv:2610.37467).