Zhengchuan Liang (UC Riverside), Xiaochen Zou (UC Riverside), Chengyu Song (UC Riverside), Zhiyun Qian (UC Riverside)

The severity of information leak (infoleak for short) in OS kernels cannot be underestimated, and various exploitation techniques have been proposed to achieve infoleak in OS kernels. Among them, memory-error-based infoleak is powerful and widely used in real-world exploits. However, existing approaches to finding memory-error-based infoleak lack the systematic reasoning about its search space and do not fully explore the search space. Consequently, they fail to exploit a large number of memory errors in the kernel. According to a theoretical modeling of memory errors, the actual search space of such approach is huge, as multiple steps could be involved in the exploitation process, and virtually any memory error can be exploited to achieve infoleak. To bridge the gap between the theory and reality, we propose a framework K-LEAK to facilitate generating memory-error-based infoleak exploits in the Linux kernel. K-LEAK considers infoleak exploit generation as a data-flow search problem. By modeling unintended data flows introduced by memory errors, and how existing memory errors can create new memory errors, K-LEAK can systematically search for infoleak data-flow paths in a multi-step manner. We implement a prototype of K-LEAK and evaluate it with memory errors from syzbot and CVEs. The evaluation results demonstrate the effectiveness of K-LEAK in generating diverse infoleak exploits using various multi-step strategies.

View More Papers

WIP: Hidden Hub Eavesdropping Attack in Matter-enabled Smart Home...

Song Liao, Jingwen Yan, Long Cheng (Clemson University)

Read More

Separation is Good: A Faster Order-Fairness Byzantine Consensus

Ke Mu (Southern University of Science and Technology, China), Bo Yin (Changsha University of Science and Technology, China), Alia Asheralieva (Loughborough University, UK), Xuetao Wei (Southern University of Science and Technology, China & Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, SUSTech, China)

Read More

Facilitating Non-Intrusive In-Vivo Firmware Testing with Stateless Instrumentation

Jiameng Shi (University of Georgia), Wenqiang Li (Independent Researcher), Wenwen Wang (University of Georgia), Le Guan (University of Georgia)

Read More

Low-Quality Training Data Only? A Robust Framework for Detecting...

Yuqi Qing (Tsinghua University), Qilei Yin (Zhongguancun Laboratory), Xinhao Deng (Tsinghua University), Yihao Chen (Tsinghua University), Zhuotao Liu (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Jia Zhang (Tsinghua University), Qi Li (Tsinghua University)

Read More