Towards Efficient Heap Overflow Discovery


Xiangkun Jia, TCA/SKLCS, Institute of Software, Chinese Academy of Sciences; Chao Zhang, Institute for Network Science and Cyberspace, Tsinghua University; Purui Su, Yi Yang, Huafeng Huang, and Dengguo Feng, TCA/SKLCS, Institute of Software, Chinese Academy of Sciences


Heap overflow is a prevalent memory corruption vulnerability, playing an important role in recent attacks. Finding such vulnerabilities in applications is thus critical for security. Many state-of-art solutions focus on runtime detection, requiring abundant inputs to explore program paths in order to reach a high code coverage and luckily trigger security violations. It is likely that the inputs being tested could exercise vulnerable program paths, but fail to trigger (and thus miss) vulnerabilities in these paths. Moreover, these solutions may also miss heap vulnerabilities due to incomplete vulnerability models.

In this paper, we propose a new solution HOTracer to discover potential heap vulnerabilities. We model heap overflows as spatial inconsistencies between heap allocation and heap access operations, and perform an in-depth offline analysis on representative program execution traces to identify heap overflows. Combining with several optimizations, it could efficiently find heap overflows that are hard to trigger in binary programs. We implemented a prototype of HOTracer, evaluated it on 17 real world applications, and found 47 previously unknown heap vulnerabilities, showing its effectiveness.

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@inproceedings {203682,
author = {Xiangkun Jia and Chao Zhang and Purui Su and Yi Yang and Huafeng Huang and Dengguo Feng},
title = {Towards Efficient Heap Overflow Discovery},
booktitle = {26th USENIX Security Symposium (USENIX Security 17)},
year = {2017},
isbn = {978-1-931971-40-9},
address = {Vancouver, BC},
pages = {989--1006},
url = {},
publisher = {USENIX Association},
month = aug

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