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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MES: Thwarting Fuzzing by Suppressing Memory Errors (Registered Report)

Fannv He (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, China, and School of Cyberspace Security, Hainan University, China), Yuan Liu (School of Cyber Engineering, Xidian University, China), Jice Wang (School of Cyberspace Security, Hainan University, China), Baiquan Wang (School of Cyberspace Security, Hainan University, China), Zezhong Ren (National Computer Network…

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Causal-Guided Detoxify Backdoor Attack of Open-Weight LoRA Models

Linzhi Chen (ShanghaiTech University), Yang Sun (Independent Researcher), Hongru Wei (ShanghaiTech University), Yuqi Chen (ShanghaiTech University)

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