Fangming Gu (Institute of Information Engineering, Chinese Academy of Sciences), Qingli Guo (Institute of Information Engineering, Chinese Academy of Sciences), Jie Lu (Institute of Computing Technology, Chinese Academy of Sciences), Qinghe Xie (Institute of Information Engineering, Chinese Academy of Sciences), Beibei Zhao (Institute of Information Engineering, Chinese Academy of Sciences), Kangjie Lu (University of Minnesota), Hong Li (Institute of information engineering, Chinese Academy of Sciences), Xiaorui Gong (Institute of information engineering, Chinese Academy of Sciences)

The Windows operating system employs various inter-process communication (IPC) mechanisms, typically involving a privileged server and a less privileged client. However, scenarios exist where the client has higher privileges, such as a performance monitor running as a domain controller obtaining data from a domain member via IPC. In these cases, the server can be compromised and send crafted data to the client.
Despite the increase in Windows client applications, existing research has overlooked potential client-side vulnerabilities, which can be equally harmful. This paper introduces GLEIPNIR, the first vulnerability detection tool for Windows remote IPC clients. GLEIPNIR identifies client-side vulnerabilities by fuzzing IPC call return values and introduces a snapshot technology to enhance testing efficiency. Experiments on 76 client applications demonstrate that GLEIPNIR can identify 25 vulnerabilities within 7 days, resulting in 14 CVEs and a bounty of $36,000.

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Try to Poison My Deep Learning Data? Nowhere to...

Yansong Gao (The University of Western Australia), Huaibing Peng (Nanjing University of Science and Technology), Hua Ma (CSIRO's Data61), Zhi Zhang (The University of Western Australia), Shuo Wang (Shanghai Jiao Tong University), Rayne Holland (CSIRO's Data61), Anmin Fu (Nanjing University of Science and Technology), Minhui Xue (CSIRO's Data61), Derek Abbott (The University of Adelaide, Australia)

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Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

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VoiceRadar: Voice Deepfake Detection using Micro-Frequency and Compositional Analysis

Kavita Kumari (Technical University of Darmstadt), Maryam Abbasihafshejani (University of Texas at San Antonio), Alessandro Pegoraro (Technical University of Darmstadt), Phillip Rieger (Technical University of Darmstadt), Kamyar Arshi (Technical University of Darmstadt), Murtuza Jadliwala (University of Texas at San Antonio), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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