Angora: Efficient Fuzzing by Principled Search

doi:10.1109/SP.2018.00046

	Angora: Efficient Fuzzing by Principled Search
	Chen, Peng1 ; Chen, Hao 2
	2018
会议录名称	2018 IEEE SYMPOSIUM ON SECURITY AND PRIVACY (SP)
ISSN	2375-1207
卷号	2018-May
页码	711-725
发表状态	已发表
DOI	10.1109/SP.2018.00046
摘要	Fuzzing is a popular technique for finding software bugs. However, the performance of the state-of-the-art fuzzers leaves a lot to be desired. Fuzzers based on symbolic execution produce quality inputs but run slow, while fuzzers based on random mutation run fast but have difficulty producing quality inputs. We propose Angora, a new mutation-based fuzzer that outperforms the state-of-the-art fuzzers by a wide margin. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution. To solve path constraints efficiently, we introduce several key techniques: scalable byte-level taint tracking, context-sensitive branch count, search based on gradient descent, and input length exploration. On the LAVA-M data set, Angora found almost all the injected bugs, found more bugs than any other fuzzer that we compared with, and found eight times as many bugs as the second-best fuzzer in the program who. Angora also found 103 bugs that the LAVA authors injected but could not trigger. We also tested Angora on eight popular, mature open source programs. Angora found 6, 52, 29, 40 and 48 new bugs in file, jhead, nm, objdump and size, respectively. We measured the coverage of Angora and evaluated how its key techniques contribute to its impressive performance.
出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
会议地点	San Francisco, CA, United states
会议日期	20-24 May 2018
URL	查看原文
收录类别	EI ; CPCI-S ; CPCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000442163200042
出版者	IEEE
EI入藏号	20183205666090
EI主题词	Model checking ; Open source software
EI分类号	Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Computer Software, Data Handling and Applications:723 ; Computer Programming:723.1
原始文献类型	Proceedings Paper
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/27574
专题	信息科学与技术学院信息科学与技术学院_硕士生
通讯作者	Chen, Peng
作者单位	1.ShanghaiTech Univ, Shanghai, Peoples R China 2.Univ Calif Davis, Davis, CA 95616 USA
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Chen, Peng,Chen, Hao. Angora: Efficient Fuzzing by Principled Search[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2018:711-725.