Yuan Li (Zhongguancun Laboratory & Tsinghua University), Chao Zhang (Tsinghua University & JCSS & Zhongguancun Laboratory), Jinhao Zhu (UC Berkeley), Penghui Li (Zhongguancun Laboratory), Chenyang Li (Peking University), Songtao Yang (Zhongguancun Laboratory), Wende Tan (Tsinghua University)

Despite the high frequency of vulnerabilities exposed in software, patching these vulnerabilities remains slow and challenging, which leaves a potential attack window. To mitigate this threat, researchers seek temporary solutions to prevent vulnerabilities from being exploited or triggered before they are officially patched. However, prior approaches have limited protection scope, often require code modification of the target vulnerable programs, and rely on recent system features. These limitations significantly reduce their usability and practicality.

In this work, we introduce VulShield, an automated temporary protection system that addresses these limitations. VulShield leverages sanitizer reports, and automatically generates security policies that describe the vulnerability triggering conditions. The policies are then enforced through a Linux kernel module that can efficiently detect and prevent vulnerability from being triggered or exploited at runtime. By carefully designing the kernel module, VulShield is capable of protecting both vulnerable kernels and user-space programs running on them. It does not rely on recent system features like eBPF and Linux security modules. VulShield is also pluggable and non-invasive as it does not need to modify the code of target vulnerable software. We evaluated
VulShield’s capability in a comprehensive set of vulnerabilities in 9 different types and found that VulShield mitigated all cases in an automated and effective manner. For Nginx, the latency introduced per request does not exceed 0.001 ms, while the peak performance overhead observed in UnixBench is 1.047%.

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Yong Zhuang (Wuhan University), Keyan Guo (University at Buffalo), Juan Wang (Wuhan University), Yiheng Jing (Wuhan University), Xiaoyang Xu (Wuhan University), Wenzhe Yi (Wuhan University), Mengda Yang (Wuhan University), Bo Zhao (Wuhan University), Hongxin Hu (University at Buffalo)

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URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Duanyi Yao (Hong Kong University of Science and Technology), Songze Li (Southeast University), Xueluan Gong (Wuhan University), Sizai Hou (Hong Kong University of Science and Technology), Gaoning Pan (Hangzhou Dianzi University)

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WIP: Towards Privacy Compliance by Design in the Matter...

Yichen Liu (Indiana University Bloomington), Jingwen Yan (Clemson University), Song Liao (Texas Tech University), Long Cheng (Clemson University), Luyi Xing (Indiana University Bloomington)

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Be Careful of What You Embed: Demystifying OLE Vulnerabilities

Yunpeng Tian (Huazhong University of Science and Technology), Feng Dong (Huazhong University of Science and Technology), Haoyi Liu (Huazhong University of Science and Technology), Meng Xu (University of Waterloo), Zhiniang Peng (Huazhong University of Science and Technology; Sangfor Technologies Inc.), Zesen Ye (Sangfor Technologies Inc.), Shenghui Li (Huazhong University of Science and Technology), Xiapu Luo…

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