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<\/a><\/p>\n\u8bb2\u8005<\/strong>\uff1aPeng Chen and Hao Chen<\/p>\n\u6458\u8981<\/strong>\uff1aFuzzing is a popular technique for finding software bugs. However, 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.<\/p>\n\u4e2a\u4eba\u4ecb\u7ecd:<\/strong><\/p>\nHao Chen is a professor at the Department of Computer Science at the University of California, Davis. He received his PhD at the Computer Science Division at the University of California, Berkeley. His current research focuses on software security, machine learning, and their synergy. He won the National Science Foundation CAREER award in 2007, and UC Davis College of Engineering Faculty Award in 2010.<\/p>\n
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