See, stats, and : https : / / www . researchgate . net / publication / 311886374 VUzzer : Application - aware Conference DOI : 10 . 14722 / ndss . 2017 . 23404 CITATIONS 0 READS 17 6 , including : Some : Systems Sanjay Vrije , Amsterdam , Netherlands 38 SEE Ashish International 1 SEE Cristiano VU 51 SEE Herbert VU 163 , 836 SEE All . The . All - text and , letting . Abstract—Fuzzing is an effective software testing technique to find bugs . Given the size and complexity of real - world applications , modern fuzzers tend to be either scalable , but not effective in exploring bugs that lie deeper in the execution , or capable of penetrating deeper in the application , but not scalable . In this paper , we present an application - aware evolutionary fuzzing strategy that does not require any prior knowledge of the application or input format . In order to maximize coverage and explore deeper paths , we leverage control - and data - flow features based on static and dynamic analysis to infer fundamental prop - erties of the application . This enables much faster generation of interesting inputs compared to an application - agnostic approach . We implement our fuzzing strategy in VUzzer and evaluate it on three different datasets : DARPA Grand Challenge binaries (CGC) , a set of real - world applications (binary input parsers) , and the recently released LAVA dataset . On all of these datasets , VUzzer yields significantly better results than state - of - the - art fuzzers , by quickly finding several existing and new bugs .