Virtual Reality and Augmented Reality Security: A Reconnaissance and Vulnerability Assessment Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Various industries have widely adopted Virtual Reality (VR) and Augmented Reality (AR) technologies to enhance productivity and user experiences. However, their integration introduces significant security challenges. This systematic literature review focuses on identifying devices used in AR and VR technologies and specifies the associated vulnerabilities, particularly during the reconnaissance phase and vulnerability assessment, which are critical steps in penetration testing. Following Kitchenham and Charters' guidelines, we systematically selected and analyzed primary studies. The reconnaissance phase involves gathering detailed information about AR and VR systems to identify potential attack vectors. In the vulnerability assessment phase, these vectors are analyzed to pinpoint weaknesses that malicious actors could exploit. Our findings reveal that AR and VR devices, such as headsets (e.g., HTC Vive, Oculus Quest), development platforms (e.g., Unity Framework, Google Cardboard SDK), and applications (e.g., Bigscreen VR, VRChat), are susceptible to various attacks, including remote code execution, cross-site scripting (XSS), eavesdropping, and man-in-the-room attacks. Specifically, the Bigscreen VR application exhibited severe vulnerabilities like remote code execution (RCE) via the 'Application.OpenURL' API, XSS in user inputs, and botnet propagation. Similarly, the Oculus Quest demonstrated susceptibility to side-channel attacks and ransomware. This paper provides a detailed overview of specific device vulnerabilities and emphasizes the importance of the initial steps in penetration testing to identify security weaknesses in AR and VR systems. By highlighting these vulnerabilities, we aim to assist researchers in exploring and mitigating these security challenges, ensuring the safe deployment and use of AR and VR technologies across various sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it