Cerebro: Context-aware adaptive fuzzing for effective vulnerability detection

• Main Authors: LI, Yuekang, XUE, Yinxing, CHEN, Hongxu, WU, Xiuheng, ZHANG, Cen, XIE, Xiaofei, WANG, Haijun, LIU, Yang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Fuzz Testing; Software Vulnerability; OS and Networks; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7072; https://ink.library.smu.edu.sg/context/sis_research/article/8075/viewcontent/3338906.3338975.pdf
• Institution: Singapore Management University

Existing greybox fuzzers mainly utilize program coverage as the goal to guide the fuzzing process. To maximize their outputs, coverage-based greybox fuzzers need to evaluate the quality of seeds properly, which involves making two decisions: 1) which is the most promising seed to fuzz next (seed pri...