Automatically assessing crashes from heap overflows

Liang He, Yan Cai, Hong Hu, Purui Su, Zhenkai Liang, Yi Yang, Huafeng Huang, Jia Yan, Xiangkun Jia, Dengguo Feng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Heap overflow is one of the most widely exploited vulnerabilities, with a large number of heap overflow instances reported every year. It is important to decide whether a crash caused by heap overflow can be turned into an exploit. Efficient and effective assessment of exploitability of crashes facilitates to identify severe vulnerabilities and thus prioritize resources. In this paper, we propose the first metrics to assess heap overflow crashes based on both the attack aspect and the feasibility aspect. We further present HCSIFTER, a novel solution to automatically assess the exploitability of heap overflow instances under our metrics. Given a heap-based crash, HCSIFTER accurately detects heap overflows through dynamic execution without any source code or debugging information. Then it uses several novel methods to extract program execution information needed to quantify the severity of the heap overflow using our metrics. We have implemented a prototype HCSIFTER and applied it to assess nine programs with heap overflow vulnerabilities. HCSIFTER successfully reports that five heap overflow vulnerabilities are highly exploitable and two overflow vulnerabilities are unlikely exploitable. It also gave quantitatively assessments for other two programs. On average, it only takes about two minutes to assess one heap overflow crash. The evaluation result demonstrates both effectiveness and efficiency of HC Sifter.

Original languageEnglish (US)
Title of host publicationASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering
EditorsTien N. Nguyen, Grigore Rosu, Massimiliano Di Penta
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages274-279
Number of pages6
ISBN (Electronic)9781538626849
DOIs
StatePublished - Nov 20 2017
Event32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017 - Urbana-Champaign, United States
Duration: Oct 30 2017Nov 3 2017

Publication series

NameASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering

Other

Other32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017
CountryUnited States
CityUrbana-Champaign
Period10/30/1711/3/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Optimization

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