Evaluating Code Coverage for Kernel Fuzzers via Function Call Graph

Mingi Cho, Hoyong Jin, Dohyeon An, Taekyoung Kwon

Research output: Contribution to journalArticlepeer-review

Abstract

The OS kernel, which has full system privileges, is an attractive attack surface. A kernel fuzzer that targets system calls in fuzzing is a popular tool for discovering kernel bugs that can induce kernel privilege escalation attacks. To the best of our knowledge, the relevance of code coverage, which is obtained by fuzzing, to the system call has not been studied yet. For instance, modern coverage-guided kernel fuzzers, such as Syzkaller, estimate code coverage by comparing the entire set of executed basic blocks (or edges) regardless of the system call relevancy. Our insight is that the system call relevancy could be an essential performance indicator for realizing kernel fuzzing. In this regard, this study aims to assess the system call-related code coverage of kernel fuzzers. For this purpose, we have developed a practical assessment system that leverages the Intel PT and KCOV and assessed the Linux kernel fuzzers, such as Syzkaller, Trinity, and ext4 fuzzer. The experiments on different kernel versions demonstrated that approximately 32,000-47,000 functions are implemented in the Linux kernel, and approximately 9.7-15.2% are related to the system call. Our finding is that fuzzers that achieve higher code coverage in conventional metrics do not execute more basic blocks related to system calls. Thus, we recommend that kernel fuzzers use both system call-related functions and regular basic blocks in coverage metrics to assess fuzzing performance or to improve coverage feedback.

Original languageEnglish
Pages (from-to)157267-157277
Number of pages11
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Government of Korea, Ministry of Science & Information Technology (MSIT), under Grant 2018-0-00513 (Machine Learning-Based Automation of Vulnerability Detection on Unix-Based Kernel).

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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