PriviSense: A Frida-Based Framework for Multi-Sensor Spoofing on Android
Ibrahim Khalilov, Chaoran Chen, Ziang Xiao, Tianshi Li, Toby Jia-Jun Li, Yaxing Yao

TL;DR
PriviSense is a Frida-based framework that enables real-time, on-device spoofing of sensors and system signals on rooted Android devices, facilitating reproducible testing of context-aware apps without emulators.
Contribution
It introduces a novel toolkit for scriptable, reversible sensor and system signal spoofing directly on physical devices, improving testing and analysis of context-sensitive Android apps.
Findings
Effective real-time sensor spoofing demonstrated on Android
Supports reproducible testing without emulators or app modifications
Facilitates privacy analysis and app behavior testing
Abstract
Mobile apps increasingly rely on real-time sensor and system data to adapt their behavior to user context. While emulators and instrumented builds offer partial solutions, they often fail to support reproducible testing of context-sensitive app behavior on physical devices. We present PriviSense, a Frida-based, on-device toolkit for runtime spoofing of sensor and system signals on rooted Android devices. PriviSense can script and inject time-varying sensor streams (accelerometer, gyroscope, step counter) and system values (battery level, system time, device metadata) into unmodified apps, enabling reproducible on-device experiments without emulators or app rewrites. Our demo validates real-time spoofing on a rooted Android device across five representative sensor-visualization apps. By supporting scriptable and reversible manipulation of these values, PriviSense facilitates testing of…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Context-Aware Activity Recognition Systems · Security and Verification in Computing
