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Ffuzz: Towards full system high coverage fuzz testing on binary executables

Author

Listed:
  • Bin Zhang
  • Jiaxi Ye
  • Xing Bi
  • Chao Feng
  • Chaojing Tang

Abstract

Bugs and vulnerabilities in binary executables threaten cyber security. Current discovery methods, like fuzz testing, symbolic execution and manual analysis, both have advantages and disadvantages when exercising the deeper code area in binary executables to find more bugs. In this paper, we designed and implemented a hybrid automatic bug finding tool—Ffuzz—on top of fuzz testing and selective symbolic execution. It targets full system software stack testing including both the user space and kernel space. Combining these two mainstream techniques enables us to achieve higher coverage and avoid getting stuck both in fuzz testing and symbolic execution. We also proposed two key optimizations to improve the efficiency of full system testing. We evaluated the efficiency and effectiveness of our method on real-world binary software and 844 memory corruption vulnerable programs in the Juliet test suite. The results show that Ffuzz can discover software bugs in the full system software stack effectively and efficiently.

Suggested Citation

  • Bin Zhang & Jiaxi Ye & Xing Bi & Chao Feng & Chaojing Tang, 2018. "Ffuzz: Towards full system high coverage fuzz testing on binary executables," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-16, May.
    • Handle: RePEc:plo:pone00:0196733
    DOI: 10.1371/journal.pone.0196733
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    References listed on IDEAS

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    1. Wang, Weiping & Yu, Minghui & Luo, Xiong & Liu, Linlin & Yuan, Manman & Zhao, Wenbing, 2017. "Synchronization of memristive BAM neural networks with leakage delay and additive time-varying delay components via sampled-data control," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 84-97.
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