• DocumentCode
    1797975
  • Title

    Improving guide-based vulnerability detection with hybrid symbolic execution

  • Author

    Yongji Ouyang ; Shuai Zeng ; Chao Yang ; Qingxian Wang

  • Author_Institution
    State Key Lab. of Math. Eng. & Adv. Comput., Zhengzhou, China
  • fYear
    2014
  • fDate
    15-17 Nov. 2014
  • Firstpage
    1038
  • Lastpage
    1043
  • Abstract
    Symbolic Execution is a key and useful technology in current refinement software test, but there still exists some problems such as space explosion. In order to mitigate this problem and improve the ability for detecting vulnerabilities, this paper presents the improving guide-based vulnerability detection with hybrid symbolic execution, which aims to test suspicious objects. This method conducts path traversal with a hybrid symbolic execution model, which alternates between dynamic and static symbolic execution, and verify whether it is vulnerability through summarizing the characteristics of vulnerabilities and generating a constraint expression. Experimental result shows that this method can successfully detect errors in 56 seconds, which exceeds any other modern mainstream symbolic execution tools including CUTE, KLEE, S2E and Cloud9. Compared with CUTE, this method alleviates the problem of space explosion. Besides, this papaer successfully verifies the vulnerabilities of OpenSSL and some other commonly used software.
  • Keywords
    program diagnostics; program testing; CUTE; OpenSSL; constraint expression; dynamic symbolic execution; guide-based vulnerability detection; hybrid symbolic execution; path traversal; refinement software test; space explosion problem alleviation; static symbolic execution; Algorithm design and analysis; Context; Explosions; Hybrid power systems; Refining; Software; Testing; constraint expression; hybrid symbolic execution; space explosion; test;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Informatics (ICSAI), 2014 2nd International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4799-5457-5
  • Type

    conf

  • DOI
    10.1109/ICSAI.2014.7009438
  • Filename
    7009438