Apple Vision Pro apps often share data without proper privacy warnings
AVP-Inspect: Coordinated Cyber-Physical Testing for Privacy Analysis of COTS Apple Vision Pro Applications
Cryptography and Security
Summary
Extended reality (XR) devices like the Apple Vision Pro collect lots of personal data, raising privacy concerns. Since the Apple Vision Pro is a closed system, it's hard to analyze how its apps handle user data. The authors created a system called AVP-Inspect that controls both the device's software and hardware to automatically test apps and check if they leak private information through network traffic. Testing over 300 apps showed that more than half share data in ways users are not clearly told about. This work reveals privacy risks in Apple Vision Pro applications that were previously difficult to uncover.
Extended reality (XR)Apple Vision ProPrivacy violationNetwork traffic analysisDynamic analysisCyber-physical testingClosed-source ecosystem3D user interface explorationPrivacy taxonomyAutomated testing
Authors
Yichang Xiong, Vamsi Shankar Simhadri, Yue Xiao, Xiaokuan Zhang
Abstract
XR devices introduce substantial privacy concerns due to their comprehensive data collection capabilities that surpass traditional computing platforms. While existing works have demonstrated privacy concerns on Android-based XR devices such as Meta Quest series by performing network traffic analysis, little attention has been paid to the Apple Vision Pro (AVP) devices, mainly due to the closed nature and the technical challenges associated with AVP devices. In this work, we make a bold attempt to detect privacy violations of AVP applications from network traffic through automatic testing on AVP devices. Our key insight is that effective AVP application testing requires coordinated control of both cyber (software) and physical (hardware) components, which we term Coordinated Cyber-Physical Testing. Building on this insight, we design and implement AVP-Inspect, an automatic dynamic analysis framework for AVP applications, overcoming significant challenges enforced by the closed-source nature of AVP ecosystem. AVP-Inspect consists of three components: an automatic device controller by building customized hardware devices, a 3D UI explorer by designing a new exploration engine, and a privacy violation detector by constructing a unified privacy taxonomy for AVP. We first evaluated AVP-Inspect on a manually constructed ground truth dataset, then performed a large-scale analysis on 324 AVP applications downloaded from the App Store, with each app tested for 20 minutes. We found that 188 (58.0%) of apps exhibit at least one violation, and more than 60% of the network traffic flows are not properly disclosed.