Razzer: Finding Kernel Race Bugs through Fuzzing
Abstrak
A data race in a kernel is an important class of bugs, critically impacting the reliability and security of the associated system. As a result of a race, the kernel may become unresponsive. Even worse, an attacker may launch a privilege escalation attack to acquire root privileges. In this paper, we propose Razzer, a tool to find race bugs in kernels. The core of Razzer is in guiding fuzz testing towards potential data race spots in the kernel. Razzer employs two techniques to find races efficiently: a static analysis and a deterministic thread interleaving technique. Using a static analysis, Razzer identifies over-approximated potential data race spots, guiding the fuzzer to search for data races in the kernel more efficiently. Using the deterministic thread interleaving technique implemented at the hypervisor, Razzer tames the non-deterministic behavior of the kernel such that it can deterministically trigger a race. We implemented a prototype of Razzer and ran the latest Linux kernel (from v4.16-rc3 to v4.18-rc3) using Razzer. As a result, Razzer discovered 30 new races in the kernel, with 16 subsequently confirmed and accordingly patched by kernel developers after they were reported.
Topik & Kata Kunci
Penulis (5)
Dae R. Jeong
Kyungtae Kim
B. Shivakumar
Byoungyoung Lee
I. Shin
Akses Cepat
- Tahun Terbit
- 2019
- Bahasa
- en
- Total Sitasi
- 200×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/SP.2019.00017
- Akses
- Open Access ✓