Tao Ni (City University of Hong Kong), Yuefeng Du (City University of Hong Kong), Qingchuan Zhao (City University of Hong Kong), Cong Wang (City University of Hong Kong)

Virtual Reality (VR) technologies are increasingly employed in numerous applications across various areas. Therefore, it is essential to ensure the security of interactions between users and VR devices. In this paper, we disclose a new side-channel leakage in the constellation tracking system of mainstream VR platforms, where the infrared (IR) signals emitted from the VR controllers for controller-headset interactions can be maliciously exploited to reconstruct unconstrained input keystrokes on the virtual keyboard non-intrusively. We propose a novel keystroke inference attack named VRecKey to demonstrate the feasibility and practicality of this novel infrared side channel. Specifically, VRecKey leverages a customized 2D IR sensor array to intercept ambient IR signals emitted from VR controllers and subsequently infers (i) character-level key presses on the virtual keyboard and (ii) word-level keystrokes along with their typing trajectories. We extensively evaluate the effectiveness of VRecKey with two commercial VR devices, and the results indicate that it can achieve over 94.2% and 90.5% top-3 accuracy in inferring character-level and word-level keystrokes with varying lengths, respectively. In addition, empirical results show that VRecKey is resilient to several practical impact factors and presents effectiveness in various real-world scenarios, which provides a complementary and orthogonal attack surface for the exploration of keystroke inference attacks in VR platforms.

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Evaluating LLMs Towards Automated Assessment of Privacy Policy Understandability

Keika Mori (Deloitte Tohmatsu Cyber LLC, Waseda University), Daiki Ito (Deloitte Tohmatsu Cyber LLC), Takumi Fukunaga (Deloitte Tohmatsu Cyber LLC), Takuya Watanabe (Deloitte Tohmatsu Cyber LLC), Yuta Takata (Deloitte Tohmatsu Cyber LLC), Masaki Kamizono (Deloitte Tohmatsu Cyber LLC), Tatsuya Mori (Waseda University, NICT, RIKEN AIP)

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LADDER: Multi-Objective Backdoor Attack via Evolutionary Algorithm

Dazhuang Liu (Delft University of Technology), Yanqi Qiao (Delft University of Technology), Rui Wang (Delft University of Technology), Kaitai Liang (Delft University of Technology), Georgios Smaragdakis (Delft University of Technology)

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SketchFeature: High-Quality Per-Flow Feature Extractor Towards Security-Aware Data Plane

Sian Kim (Ewha Womans University), Seyed Mohammad Mehdi Mirnajafizadeh (Wayne State University), Bara Kim (Korea University), Rhongho Jang (Wayne State University), DaeHun Nyang (Ewha Womans University)

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CounterSEVeillance: Performance-Counter Attacks on AMD SEV-SNP

Stefan Gast (Graz University of Technology), Hannes Weissteiner (Graz University of Technology), Robin Leander Schröder (Fraunhofer SIT, Darmstadt, Germany and Fraunhofer Austria, Vienna, Austria), Daniel Gruss (Graz University of Technology)

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