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

MineShark: Cryptomining Traffic Detection at Scale

Shaoke Xi (Zhejiang University), Tianyi Fu (Zhejiang University), Kai Bu (Zhejiang University), Chunling Yang (Zhejiang University), Zhihua Chang (Zhejiang University), Wenzhi Chen (Zhejiang University), Zhou Ma (Zhejiang University), Chongjie Chen (HANG ZHOU CITY BRAIN CO., LTD), Yongsheng Shen (HANG ZHOU CITY BRAIN CO., LTD), Kui Ren (Zhejiang University)

Read More

SCAMMAGNIFIER: Piercing the Veil of Fraudulent Shopping Website Campaigns

Marzieh Bitaab (Arizona State University), Alireza Karimi (Arizona State University), Zhuoer Lyu (Arizona State University), Adam Oest (Amazon), Dhruv Kuchhal (Amazon), Muhammad Saad (X Corp.), Gail-Joon Ahn (Arizona State University), Ruoyu Wang (Arizona State University), Tiffany Bao (Arizona State University), Yan Shoshitaishvili (Arizona State University), Adam Doupé (Arizona State University)

Read More

NodeMedic-FINE: Automatic Detection and Exploit Synthesis for Node.js Vulnerabilities

Darion Cassel (Carnegie Mellon University), Nuno Sabino (IST & CMU), Min-Chien Hsu (Carnegie Mellon University), Ruben Martins (Carnegie Mellon University), Limin Jia (Carnegie Mellon University)

Read More