Fuzzing technologies have evolved at a fast pace in recent years, revealing bugs in programs with ever increasing depth and speed. Applications working with complex formats are however more difficult to take on, as inputs need to meet certain format-specific characteristics to get through the initial parsing stage and reach deeper behaviors of the program. Unlike prior proposals based on manually written format specifications, we propose a technique to automatically generate and mutate inputs for unknown chunk-based binary formats. We identify dependencies between input bytes and comparison instructions, and use them to assign tags that characterize the processing logic of the program. Tags become the building block for structure-aware mutations involving chunks and fields of the input. Our technique can perform comparably to structure-aware fuzzing proposals that require human assistance. Our prototype implementation WEIZZ revealed 16 unknown bugs in widely used programs.

WEIZZ: Automatic grey-box fuzzing for structured binary formats / Fioraldi, A.; D'Elia, D. C.; Coppa, Emilio. - ISSTA 2020: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, (2020), pp. 1-13. (29th ACM SIGSOFT International Symposium on Software Testing and Analysis, Los Angeles, USA, 18-22 luglio 2020). [10.1145/3395363.3397372].

WEIZZ: Automatic grey-box fuzzing for structured binary formats

Coppa E.
2020

Abstract

Fuzzing technologies have evolved at a fast pace in recent years, revealing bugs in programs with ever increasing depth and speed. Applications working with complex formats are however more difficult to take on, as inputs need to meet certain format-specific characteristics to get through the initial parsing stage and reach deeper behaviors of the program. Unlike prior proposals based on manually written format specifications, we propose a technique to automatically generate and mutate inputs for unknown chunk-based binary formats. We identify dependencies between input bytes and comparison instructions, and use them to assign tags that characterize the processing logic of the program. Tags become the building block for structure-aware mutations involving chunks and fields of the input. Our technique can perform comparably to structure-aware fuzzing proposals that require human assistance. Our prototype implementation WEIZZ revealed 16 unknown bugs in widely used programs.
2020
9781450380089
binary testing, chunk-based formats, Fuzzing, structural mutations
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/236290
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