AVP-Inspect: Coordinated Cyber-Physical Testing for Privacy Analysis of COTS Apple Vision Pro Applications
AVP-Inspect automated testing finds 58% of 324 Apple Vision Pro apps show privacy violations, with over 60% of network traffic flows undisclosed.
Researchers built AVP-Inspect, a dynamic analysis framework combining custom hardware device control, 3D UI exploration, and a unified privacy taxonomy for Apple Vision Pro. Testing 324 App Store apps for 20 minutes each found 188 (58.0%) with at least one privacy violation. More than 60% of observed network traffic flows were not properly disclosed, extending prior XR privacy work beyond Android-based devices such as Meta Quest.
- First large-scale automated privacy analysis of Apple Vision Pro applications via network traffic
- Coordinates cyber and physical control to overcome the closed AVP ecosystem
- Finds 188 of 324 apps (58.0%) exhibit at least one privacy violation
- Over 60% of network traffic flows lack proper disclosure
- Constructs a unified privacy taxonomy for AVP applications
Full article237 words · extracted from arxiv.org · click to collapse
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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.08103