GAZEploit: Remote Keystroke Inference Attack by Gaze Estimation from Avatar Views in VR/MR Devices

TL;DR

GAZEploit is a novel remote attack exploiting eye-tracking data in VR/MR devices to infer keystrokes, revealing significant security vulnerabilities in current gaze-controlled typing methods.

Contribution

This paper introduces GAZEploit, the first attack leveraging avatar view-based gaze estimation to steal keystrokes remotely in VR/MR environments.

Findings

Achieved over 80% keystroke inference accuracy.

Identified 15+ vulnerable apps in the Apple Store.

Demonstrated practicality of the attack across various scenarios.

Abstract

The advent and growing popularity of Virtual Reality (VR) and Mixed Reality (MR) solutions have revolutionized the way we interact with digital platforms. The cutting-edge gaze-controlled typing methods, now prevalent in high-end models of these devices, e.g., Apple Vision Pro, have not only improved user experience but also mitigated traditional keystroke inference attacks that relied on hand gestures, head movements and acoustic side-channels. However, this advancement has paradoxically given birth to a new, potentially more insidious cyber threat, GAZEploit. In this paper, we unveil GAZEploit, a novel eye-tracking based attack specifically designed to exploit these eye-tracking information by leveraging the common use of virtual appearances in VR applications. This widespread usage significantly enhances the practicality and feasibility of our attack compared to existing methods. GAZEploit takes advantage of this vulnerability to remotely extract gaze estimations and steal sensitive keystroke information across various typing scenarios-including messages, passwords, URLs, emails, and passcodes. Our research, involving 30 participants, achieved over 80% accuracy in keystroke inference. Alarmingly, our study also identified over 15 top-rated apps in the Apple Store as vulnerable to the GAZEploit attack, emphasizing the urgent need for bolstered security measures for this state-of-the-art VR/MR text entry method.