An Empirical Study on Virtual Reality Software Security Weaknesses

• URL: http://arxiv.org/abs/2507.17324v2
• Date: Thu, 24 Jul 2025 03:05:47 GMT
• Title: An Empirical Study on Virtual Reality Software Security Weaknesses
• Authors: Yifan Xu, Jinfu Chen, Zhenyu Qi, Huashan Chen, Junyi Wang, Pengfei Hu, Feng Liu, Sen He,
• Abstract summary: This study investigates 334 VR projects hosted on GitHub, examining 1,681 software security weaknesses.<n>Due to the limited availability of VR software security weaknesses in public databases (e.g., the National Vulnerability Database or NVD), we prepare the first systematic dataset of VR software security weaknesses.<n>Our empirical study on the dataset leads to useful insights, including: (i) VR weaknesses are heavily skewed toward user interface weaknesses, followed by resource-related weaknesses; (ii) VR development tools pose higher security risks than VR applications; (iii) VR security weaknesses are often introduced at the VR software birth time.
• Score: 21.92000678499958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Virtual Reality (VR) has emerged as a transformative technology across industries, yet its security weaknesses, including vulnerabilities, are underinvestigated. This study investigates 334 VR projects hosted on GitHub, examining 1,681 software security weaknesses to understand: what types of weaknesses are prevalent in VR software; when and how weaknesses are introduced; how long they have survived; and how they have been removed. Due to the limited availability of VR software security weaknesses in public databases (e.g., the National Vulnerability Database or NVD), we prepare the first systematic dataset of VR software security weaknesses by introducing a novel framework to collect such weaknesses from GitHub commit data. Our empirical study on the dataset leads to useful insights, including: (i) VR weaknesses are heavily skewed toward user interface weaknesses, followed by resource-related weaknesses; (ii) VR development tools pose higher security risks than VR applications; (iii) VR security weaknesses are often introduced at the VR software birth time.

Related papers

• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.<n>In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• Virtual Reality and Augmented Reality Security: A Reconnaissance and Vulnerability Assessment Approach [0.0]
  Various industries have widely adopted Virtual Reality (VR) and Augmented Reality (AR) technologies to enhance productivity and user experiences. This systematic literature review focuses on identifying devices used in AR and VR technologies and specifies the associated vulnerabilities.
  arXiv  Detail & Related papers  (2024-07-22T18:51:59Z)
• Enabling Developers, Protecting Users: Investigating Harassment and Safety in VR [7.404772554852628]
  This study examines users' perceptions of safety control usability and effectiveness as well as the challenges that developers face in designing and deploying VR safety controls. We identify challenges VR users face while employing safety controls, such as finding users in crowded virtual spaces to block them. We emphasize the importance of establishing technical and legal guidelines to enhance user safety in virtual environments.
  arXiv  Detail & Related papers  (2024-03-08T18:15:53Z)
• An Empirical Study on Oculus Virtual Reality Applications: Security and Privacy Perspectives [46.995904896724994]
  This paper develops a security and privacy assessment tool, namely the VR-SP detector for VR apps. Using the VR-SP detector, we conduct a comprehensive empirical study on 500 popular VR apps. We find that a number of security vulnerabilities and privacy leaks widely exist in VR apps.
  arXiv  Detail & Related papers  (2024-02-21T13:53:25Z)
• Immersed in Reality Secured by Design -- A Comprehensive Analysis of Security Measures in AR/VR Environments [0.0]
  Data theft and other forms of cyberattack pose serious threats to virtual reality systems. As a result, AR systems are just as vulnerable as any other Internet of Things (IoT) device we use on a daily basis. Cybercriminals can exploit virtual reality headsets just like any other computer system.
  arXiv  Detail & Related papers  (2024-01-30T21:24:52Z)
• Deep Motion Masking for Secure, Usable, and Scalable Real-Time Anonymization of Virtual Reality Motion Data [49.68609500290361]
  Recent studies have demonstrated that the motion tracking "telemetry" data used by nearly all VR applications is as uniquely identifiable as a fingerprint scan. We present in this paper a state-of-the-art VR identification model that can convincingly bypass known defensive countermeasures.
  arXiv  Detail & Related papers  (2023-11-09T01:34:22Z)
• Security and Privacy in Virtual Reality: A Literature Survey [0.0]
  We explore the state-of-the-art in VR privacy and security, we categorise potential issues and threats, and we analyse causes and effects of the identified threats.<n>We focus on the research previously conducted in the field of authentication in VR, as it stands as the most investigated area in the topic.<n>We also provide an overview of other interesting uses of VR in the field of cybersecurity, such as the use of VR to teach cybersecurity or evaluate the usability of security solutions.
  arXiv  Detail & Related papers  (2022-04-30T08:45:09Z)
• Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022 [55.573187938617636]
  The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
  arXiv  Detail & Related papers  (2022-02-28T18:27:41Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)
• Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality [102.4151387131726]
  We propose a learning-based Incentive Mechanism framework for VR services in the Metaverse. First, we propose the quality of perception as the metric for VR users in the virtual world. Second, for quick trading of VR services between VR users (i.e., buyers) and VR SPs (i.e., sellers), we design a double Dutch auction mechanism. Third, for auction communication reduction, we design a deep reinforcement learning-based auctioneer to accelerate this auction process.
  arXiv  Detail & Related papers  (2021-11-07T13:02:52Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.