PriviSense: A Frida-Based Framework for Multi-Sensor Spoofing on Android

2026-01-29

AI Summary

This work addresses the challenge of conducting reproducible, context-aware testing of mobile applications on real Android devices, particularly when such apps depend on sensor and system data. The authors propose a runtime toolkit built on Frida that leverages dynamic instrumentation to enable scriptable, reversible, and real-time spoofing of diverse sensor inputs—such as accelerometer and gyroscope readings—and system values—including battery level and time—on rooted devices, without requiring application modification or emulator usage. This approach represents the first unified framework for controlled injection of multiple contextual signals, and its efficacy has been demonstrated across five representative applications, significantly facilitating context-dependent behavior analysis, privacy leakage investigations, and application logic validation.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Search and Optimization: Applications
• Intelligent Robots: Other Foundations and Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

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 app logic, uncovering of context-based behaviors, and privacy-focused analysis. To ensure ethical use, the code is shared upon request with verified researchers. Tool Guide: How to Run PriviSense on Rooted Android https://bit.ly/privisense-guide Demonstration video: https://www.youtube.com/watch?v=4Qwnogcc3pw

Problem

• sensor spoofing
• Android
• context-sensitive apps
• reproducible testing
• on-device experimentation

Innovation

• sensor spoofing
• on-device testing
• Frida
• context-aware apps
• runtime instrumentation