Kunlin Cai (University of California, Los Angeles), Jinghuai Zhang (University of California, Los Angeles), Ying Li (University of California, Los Angeles), Zhiyuan Wang (University of Virginia), Xun Chen (Independent Researcher), Tianshi Li (Northeastern University), Yuan Tian (University of California, Los Angeles)

The immersive nature of XR introduces a fundamentally different set of security and privacy (S&P) challenges due to the unprecedented user interactions and data collection that traditional paradigms struggle to mitigate. As the primary architects of XR applications, developers play a critical role in addressing novel threats. However, to effectively support developers, we must first understand how they perceive and respond to different threats. Despite the growing importance of this issue, there is a lack of in-depth, threat-aware studies that examine XR S&P from the developers’ perspective. To fill this gap, we interviewed 23 professional XR developers with a focus on emerging threats in XR. Our study addresses two research questions aiming to uncover existing problems in XR development and identify actionable paths forward.

By examining developers' perceptions of S&P threats, we found that: (1) XR development decisions (e.g., rich sensor data collection, user-generated content interfaces) are closely tied to and can amplify S&P threats, yet developers are often unaware of these risks, resulting in cognitive biases in threat perception; and (2) limitations in existing mitigation methods, combined with insufficient strategic, technical, and communication support, undermine developers' motivation, awareness, and ability to effectively address these threats. Based on these findings, we propose actionable and stakeholder-aware recommendations to improve XR S&P throughout the XR development process. This work represents the first effort to undertake a threat-aware, developer-centered study in the XR domain—an area where the immersive, data-rich nature of the XR technology introduces distinctive challenges.

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Achieving Interpretable DL-based Web Attack Detection through Malicious Payload...

Peiyang Li (INSC and the State Key Laboratory of Internet Architecture, Tsinghua University and Ant Group), Fukun Mei (INSC and the State Key Laboratory of Internet Architecture, Tsinghua University), Ye Wang (INSC and the State Key Laboratory of Internet Architecture, Tsinghua University), Zhuotao Liu (INSC and the State Key Laboratory of Internet Architecture, Tsinghua University),…

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PrivCode: When Code Generation Meets Differential Privacy

Zheng Liu (University of Virginia), Chen Gong (University of Virginia), Terry Yue Zhuo (Monash University and CSIRO's Data61), Kecen Li (University of Virginia), Weichen Yu (Carnegie Mellon University), Matt Fredrikson (Carnegie Mellon University), Tianhao Wang (University of Virginia)

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