Mark Huasong Meng (National University of Singapore), Qing Zhang (ByteDance), Guangshuai Xia (ByteDance), Yuwei Zheng (ByteDance), Yanjun Zhang (The University of Queensland), Guangdong Bai (The University of Queensland), Zhi Liu (ByteDance), Sin G. Teo (Agency for Science, Technology and Research), Jin Song Dong (National University of Singapore)

Ever since its genesis, Android has enabled apps to access data and services on mobile devices. This however involves a wide variety of user-unresettable identifiers (UUIs), e.g., the MAC address, which are associated with a device permanently. Given their privacy sensitivity, Android has tightened its UUI access policy since its version 10, in response to the increasingly strict privacy protection regulations around the world. Non-system apps are restricted from accessing them and are required to use user-resettable alternatives such as advertising IDs.

In this work, we conduct a systematic study on the effectiveness of the UUI safeguards on Android phones including both Android Open Source Project (AOSP) and Original Equipment Manufacturer (OEM) phones. To facilitate our large-scale study, we propose a set of analysis techniques that discover and assess UUI access channels. Our approach features a hybrid analysis that consists of static program analysis of Android Framework and forensic analysis of OS images to uncover access channels. These channels are then tested with differential analysis to identify weaknesses that open any attacking opportunity. We have conducted a vulnerability assessment on 13 popular phones of 9 major manufacturers, most of which are top-selling and installed with the recent Android versions. Our study reveals that UUI mishandling pervasively exists, evidenced by 51 unique vulnerabilities found (8 listed by CVE). Our work unveils the status quo of the UUI protection in Android phones, complementing the existing studies that mainly focus on apps' UUI harvesting behaviors. Our findings should raise an alert to phone manufacturers and would encourage policymakers to further extend the scope of regulations with device-level data protection.

View More Papers

BinaryInferno: A Semantic-Driven Approach to Field Inference for Binary...

Jared Chandler (Tufts University), Adam Wick (Fastly), Kathleen Fisher (DARPA)

Read More

Kids, Cats, and Control: Designing Privacy and Security Dashboard...

Jacob Abbott (Indiana University), Jayati Dev (Indiana University), DongInn Kim (Indiana University), Shakthidhar Reddy Gopavaram (Indiana University), Meera Iyer (Indiana University), Shivani Sadam (Indiana University) , Shirang Mare (Western Washington University), Tatiana Ringenberg (Purdue University), Vafa Andalibi (Indiana University), and L. Jean Camp(Indiana University)

Read More

WIP: The Feasibility of High-performance Message Authentication in Automotive...

Evan Allen (Virginia Tech), Zeb Bowden (Virginia Tech Transportation Institute), Randy Marchany (Virginia Tech), J. Scot Ransbottom (Virginia Tech)

Read More

WIP: Infrared Laser Reflection Attack Against Traffic Sign Recognition...

Takami Sato (University of California, Irvine), Sri Hrushikesh Varma Bhupathiraju (University of Florida), Michael Clifford (Toyota InfoTech Labs), Takeshi Sugawara (The University of Electro-Communications), Qi Alfred Chen (University of California, Irvine), Sara Rampazzi (University of Florida)

Read More