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%.

View More Papers

Silence False Alarms: Identifying Anti-Reentrancy Patterns on Ethereum to...

Qiyang Song (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Heqing Huang (Institute of Information Engineering, Chinese Academy of Sciences), Xiaoqi Jia (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Yuanbo Xie (Institute of Information…

Read More

ASGARD: Protecting On-Device Deep Neural Networks with Virtualization-Based Trusted...

Myungsuk Moon (Yonsei University), Minhee Kim (Yonsei University), Joonkyo Jung (Yonsei University), Dokyung Song (Yonsei University)

Read More

RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial...

Dzung Pham (University of Massachusetts Amherst), Shreyas Kulkarni (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

Read More