When VR Meets BCI: (Un)Observable Brainwave-aware Privacy Reconstruction in the Metaverse via Unrestricted Inbuilt Motion Sensors

Computer Science > Cryptography and Security [Submitted on 9 Jun 2026] When VR Meets BCI: (Un)Observable Brainwave-aware Privacy Reconstruction in the Metaverse via Unrestricted Inbuilt Motion Sensors Tao Ni, Zehua Sun, Qingchuan Zhao, Wei-Bin Lee, Cong Wang Metaverse devices, such as virtual reality (VR), have seen substantial development and widespread applications in numerous areas. Although recent studies have revealed privacy leakages in VR, these vulnerabilities were limited in the scope of observable behaviors in virtual scenes (e.g., what a user is seeing). In this work, we uncover the feasibility of going beyond the scope of observable user behaviors to unobservable brain EEG-correlated representations (e.g., what a user is perceiving) by leveraging unrestricted motion sensors in VR headsets to reconstruct brain EEG signals, a seemingly neglected but promising vector. The insight is that the inbuilt motion sensors (e.g., accelerometers) in the VR headset can capture subtle vibrations induced by pupillary responses, which are highly correlated with users' visual stimuli and in-brain perceptions. Therefore, we design and implement BraVeSpy to systematically investigate and demonstrate the feasibility of this severe privacy leakage originating from brain EEG-correlated representations reconstructed from variations of inbuilt motion sensors. Our extensive evaluation results from different VR devices show that BraVeSpy, for the first time in the Metaverse, can reveal unobservable privacy, where we successfully unveiled perceptive images in the brain with 52.0%-67.2% accuracy. In particular, we also find that BraVeSpy outperforms the current approaches that are limited to coarse-grained inference of observable behaviors and achieves over 85.0% accuracy in inferring user activity-related sensitive information, such as fingerprinting websites, apps, and streaming videos, and over 96.0% accuracy in user de-anonymization, gaze movement tracking, and virtual keystroke inference. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.10502 [cs.CR]   (or arXiv:2606.10502v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.10502 Focus to learn more Submission history From: Tao Ni [view email] [v1] Tue, 9 Jun 2026 07:28:16 UTC (7,176 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)