Revery: From Proof-of-Concept to Exploitable
Title | Revery: From Proof-of-Concept to Exploitable |
Publication Type | Conference Paper |
Year of Publication | 2018 |
Authors | Wang, Yan, Zhang, Chao, Xiang, Xiaobo, Zhao, Zixuan, Li, Wenjie, Gong, Xiaorui, Liu, Bingchang, Chen, Kaixiang, Zou, Wei |
Conference Name | Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security |
Publisher | ACM |
ISBN Number | 978-1-4503-5693-0 |
Keywords | composability, exploit, fuzzing, Metrics, pubcrawl, symbolic execution, taint analysis, Vulnerability |
Abstract | Automatic exploit generation is an open challenge. Existing solutions usually explore in depth the crashing paths, i.e., paths taken by proof-of-concept (POC) inputs triggering vulnerabilities, and generate exploits when exploitable states are found along the paths. However, exploitable states do not always exist in crashing paths. Moreover, existing solutions heavily rely on symbolic execution and are not scalable in path exploration and exploit generation. In addition, few solutions could exploit heap-based vulnerabilities. In this paper, we propose a new solution revery to search for exploitable states in paths diverging from crashing paths, and generate control-flow hijacking exploits for heap-based vulnerabilities. It adopts three novel techniques:(1) a digraph to characterize a vulnerability's memory layout and its contributor instructions;(2) a fuzz solution to explore diverging paths, which have similar memory layouts as the crashing paths, in order to search more exploitable states and generate corresponding diverging inputs;(3) a stitch solution to stitch crashing paths and diverging paths together, and synthesize EXP inputs able to trigger both vulnerabilities and exploitable states. We have developed a prototype of revery based on the binary analysis engine angr, and evaluated it on a set of 19 real world CTF (capture the flag) challenges. Experiment results showed that it could generate exploits for 9 (47%) of them, and generate EXP inputs able to trigger exploitable states for another 5 (26%) of them. |
URL | https://dl.acm.org/doi/10.1145/3243734.3243847 |
DOI | 10.1145/3243734.3243847 |
Citation Key | wang_revery:_2018 |