KUBO: Precise and Scalable Detection of User-triggerable Undefined Behavior Bugs in OS Kernel

Changming Liu (Northeastern University), Yaohui Chen (Facebook Inc.), Long Lu (Northeastern University)

Undefined Behavior bugs (UB) often refer to a wide range of programming errors that mainly reside in software implemented in relatively low-level programming languages e.g., C/C++. OS kernels are particularly plagued by UB due to their close interactions with the hardware. A triggered UB can often lead to exploitation from unprivileged userspace programs and cause critical security and reliability issues inside the OS. The previous works on detecting UB in kernels had to sacrifice precision for scalability, and in turn, suffered from extremely high false positives which severely impaired their usability.

We propose a novel static UB detector for Linux kernel, called KUBO which simultaneously achieves high precision and whole-kernel scalability. KUBO is focused on detecting critical UB that can be triggered by userspace input. The high precision comes from KUBO’s verification of the satisfiability of the UB-triggering paths and conditions. The whole-kernel scalability is enabled by an efficient inter-procedural analysis, which incrementally walks backward along callchains in an on-demand manner. We evaluate KUBO on several versions of whole Linux kernels (including drivers). KUBO found 23 critical UBs that were previously unknown in the latest Linux kernel. KUBO’s false detection rate is merely 27.5%, which is significantly lower than that of the state-of-the-art kernel UB detectors (91%). Our evaluation also shows the bug reports generated by KUBO are easy to triage.