NDSS2026

IsolatOS: Detecting Double Fetch Bugs in COTS RTOS by Re-enabling Kernel Isolation

Yingjie Cao, Xiaogang Zhu, Dean Sullivan, Haowei Yang, Lei Xue, Xian Li, Chenxiong Qian, Minrui Yan, Xiapu Luo

1 citation

Abstract

Real-time operating systems (RTOS) often expose double-fetch vulnerabilities when the kernel reads the same userspace memory location multiple times without ensuring consistency between fetches. Conventional static analysis cannot inspect proprietary, commercial off-the-shelf (COTS) RTOS kernels, and dynamic heuristics, which rely on broad time-window thresholds, suffer from high false positive rates and heavy emulation overhead. To address these challenges, we present ISOLATOS, the first hardware-supported framework for detecting doublefetch bugs in COTS RTOS. By leveraging modern CPU kernelisolation features, ISOLATOS enables kernel isolation so that cross-boundary accesses can be captured by triggering page faults. ISOLATOS then records page-fault metadata on each usermemory fetch. Finally, multiple fetches in the same system call are determined as a double-fetch bug, based on the lifecycle of system calls that ISOLATOS instruments into COTS RTOS. We evaluate ISOLATOS on three widely used RTOS, including QNX, VxWorks, and seL4, and demonstrate a 79.3× reduction in runtime overhead compared to state-of-the-art emulation-based detectors. ISOLATOS also detects double-fetch bugs with lower false positive rates than other tools. Our approach uncovers 43 previously unknown vulnerabilities in COTS RTOS (41 confirmed by vendors, 2 CVEs assigned). Additionally, we have demonstrated the real-world impact of our findings in automotive systems by exploiting them.