发表机构
George Mason University; William & Mary（乔治梅森大学; 威廉与玛丽学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对Apple Vision Pro应用隐私分析，提出协同网络-物理测试框架AVP-Inspect，通过自动设备控制、3D UI探索和隐私分类检测，在324个应用中识别出58.0%存在隐私违规。
AI 中文摘要
XR设备因其超越传统计算平台的全面数据收集能力而引发严重的隐私问题。尽管已有研究通过对网络流量分析展示了基于Android的XR设备（如Meta Quest系列）的隐私问题，但针对Apple Vision Pro（AVP）设备的关注却很少，这主要是由于AVP设备的封闭性及相关技术挑战。在本工作中，我们大胆尝试通过AVP设备上的自动测试，从网络流量中检测AVP应用的隐私违规行为。我们的关键洞察是，有效的AVP应用测试需要协同控制网络（软件）和物理（硬件）组件，我们将其称为协同网络-物理测试。基于这一洞察，我们设计并实现了AVP-Inspect，一个面向AVP应用的自动动态分析框架，克服了AVP生态系统封闭源代码特性带来的重大挑战。AVP-Inspect由三个组件组成：通过构建定制硬件设备的自动设备控制器、通过设计新探索引擎的3D用户界面探索器，以及通过构建统一的AVP隐私分类法的隐私违规检测器。我们首先在手动构建的基准真值数据集上评估了AVP-Inspect，然后对从App Store下载的324个AVP应用进行了大规模分析，每个应用测试20分钟。我们发现188个（58.0%）应用至少存在一项违规行为，且超过60%的网络流量未得到适当披露。
英文摘要
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.
CommentsTo appear in ACM CCS 2026
