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Efficient Fuzz Testing Leveraging Input, Code, and Execution by Nikolas Havrikov is a scholarly article available to read on EtoBox.

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Any kind of smart testing technique must be very efficient to be competitive with random fuzz testing. State-ofthe-art test generators are largely inferior to random testing in real world applications. This work proposes to gather and evaluate lightweight analyses that can enable the creation of an efficient and sufficiently effective analysis-assisted fuzz tester. The analyses shall leverage information sources apart from the program under test itself, such as e.g. descriptions of the targeted input format in the form of extended context-free grammars, or hardware counters. As the main contributions, an efficient framework for building fuzzers around given analyses will be created, and with its help analyses will be identified and categorized according to their performance.

Author

Nikolas Havrikov

Publisher

IEEE

Published

2017

Language

EN