Muhammad Jazlan

2papers

2 Papers

45.5CRApr 30
SST-Guard: Detecting and Characterizing Server-Side Google Analytics in the Wild

Muhammad Jazlan, Alexander Gamero-Garrido, Zubair Shafiq et al.

As web browsers increasingly restrict client-side tracking, the web tracking ecosystem is shifting from client-side to server-side tracking (SST). In SST, the browser sends tracking requests to an intermediate endpoint, which then forwards them to the tracker's endpoint, eliminating direct client-to-tracker requests. As a result, existing tracking protections that block requests to known tracker endpoints are rendered ineffective. In this paper, we investigate server-side implementation of Google Analytics, the most widely deployed third-party tracking service on the web today. We also present SST-Guard, a multi-modal, browser-based system for detecting and blocking server-side Google Analytics (sGA). Our key insight is that even when the tracker's endpoints change, sGA must necessarily still collect and share the same semantic information as client-side Google Analytics (e.g., identifiers, event metadata). Therefore, rather than detecting requests to known Google Analytics endpoints, SST-Guard aims to detect underlying artifacts of collection and sharing of these semantic values to any arbitrary endpoint. Operationalizing this insight is challenging because real-world sGA deployments commonly customize endpoints and obfuscate URLs/payloads. SST-Guard addresses this challenge using a value-template approach that employs regular expressions to match semantic value patterns across multiple modalities: network requests, cookies, and the window object. We validate SST-Guard on Tranco top-10k websites, detecting 4.02\% (403) sGA domains with over 93\% accuracy across three modalities, with network request classifier demonstrating the highest accuracy (99.8\%). By deploying SST-Guard in the wild, we find 4.21\% (6,314) of Tranco top-150k websites using sGA.

78.5CRApr 30
Tracking Conversations: Measuring Content and Identity Exposure on AI Chatbots

Muhammad Jazlan, Ethan Wang, Yash Vekaria et al.

AI chatbots are becoming a primary interface for seeking information. As their popularity grows, chatbot providers are starting to deploy advertising and analytics. Despite this, tracking on AI chatbots has not been systematically studied. We present a systematic measurement of web tracking on 20 popular AI chatbots. Under controlled settings using a sensitive prompt, we capture and compare network traffic in normal chats and, where supported, private chats. We search for exposure of two categories of information: content, including prompts, prompt-derived titles, chat URLs, and chat identifiers; and identity, including names, emails, account identifiers, first-party cookies, and explicit IP/User-Agent fields in payloads. We find that 17 of 20 chatbots share information with at least one third party. Three chatbots share plaintext conversation text, including both prompt and response snippets, with Microsoft Clarity through session replay. Fifteen chatbots share conversation URLs or chat identifiers with third-party advertising, analytics, or social endpoints. Several chatbots expose user identity through support widgets, analytics, advertising, and session replay tags; in some cases, hashed emails are shared.