Automatic Test Pattern Generator for Fuzzing Based on Finite State Machine

Wang, Ming-Hung; Wang, Hanchi; Chen, You-Ru; Lei, Chin-Laung

doi:10.1155/2017/7819590

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Besides context-free grammars, fuzzers have used alternate input specifications, such as regular languages (i.e. finite state automata) [9,43], or constraint languages [12]. MoWF [30] leverages a specification expressed as a constraint over the input space.…”

Section: Other Specification Languagesmentioning

confidence: 99%

FormatFuzzer: Effective Fuzzing of Binary File Formats

Dutra¹,

Gopinath²,

Zeller³

2021

Preprint

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Effective fuzzing of programs that process structured binary inputs, such as multimedia files, is a challenging task, since those programs expect a very specific input format. Existing fuzzers, however, are mostly formatagnostic, which makes them versatile, but also ineffective when a specific format is required.We present FormatFuzzer, a generator for format-specific fuzzers. FormatFuzzer takes as input a binary template (a format specification used by the 010 Editor) and compiles it into C++ code that acts as parser, mutator, and highly efficient generator of inputs conforming to the rules of the language.The resulting format-specific fuzzer can be used as a standalone producer or mutator in black-box settings, where no guidance from the program is available. In addition, by providing mutable decision seeds, it can be easily integrated with arbitrary format-agnostic fuzzers such as AFL to make them format-aware. In our evaluation on complex formats such as MP4 or ZIP, FormatFuzzer showed to be a highly effective producer of valid inputs that also detected previously unknown memory errors in ffmpeg and timidity.CCS Concepts: • Software and its engineering → Software testing and debugging; Translator writing systems and compiler generators; Parsers; Syntax.

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Section: Other Specification Languagesmentioning

confidence: 99%

FormatFuzzer: Effective Fuzzing of Binary File Formats

Dutra¹,

Gopinath²,

Zeller³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…It has been shown that different fuzz techniques can find different types of bugs in the same software system [7]. There are some fuzzers that use finite state machines to model the software and apply fuzz tests to the model [8], [9]. This suggests that fuzz testing can be beneficial to a particular context, such as using state machines to define robotic behavior.…”

Section: Related Workmentioning

confidence: 99%

Fuzz Testing in Behavior-Based Robotics

Delgado

Campusano

Bergel

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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The behavior of a robot is typically expressed as a set of source code files written using a programming language. As for any software engineering activity, programming robotic behaviors is a complex and error-prone task. This paper propose a methodology that aims to reduce the cost of producing a reliable software describing a robotic behavior by automatically testing it.We employ a fuzz testing technique to stress software components with randomly generated data. By applying fuzz testing to a complex robotic-software, we identified errors related to the coding, the way data is handled, the logic of the robotic behavior, and the initialization of architectural components. Furthermore, a panel of experts acquainted with the analyzed behavior have highlighted the relevance and the significance of our findings. Our fuzzer operates on the SMACH and ROS frameworks and it is available under the MIT public open source license.

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FormatFuzzer : Effective Fuzzing of Binary File Formats

Dutra,

Gopinath,

Zeller

2023

ACM Trans. Softw. Eng. Methodol.

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Effective fuzzing of programs that process structured binary inputs, such as multimedia files, is a challenging task, since those programs expect a very specific input format. Existing fuzzers, however, are mostly format-agnostic, which makes them versatile, but also ineffective when a specific format is required. We present FormatFuzzer , a generator for format-specific fuzzers . FormatFuzzer takes as input a binary template (a format specification used by the 010 Editor) and compiles it into C++ code that acts as parser, mutator, and highly efficient generator of inputs conforming to the rules of the language. The resulting format-specific fuzzer can be used as a standalone producer or mutator in black-box settings, where no guidance from the program is available. In addition, by providing mutable decision seeds, it can be easily integrated with arbitrary format-agnostic fuzzers such as AFL to make them format-aware. In our evaluation on complex formats such as MP4 or ZIP, FormatFuzzer showed to be a highly effective producer of valid inputs that also detected previously unknown memory errors in ffmpeg and timidity .

show abstract

Automatic Test Pattern Generator for Fuzzing Based on Finite State Machine

Cited by 3 publications

References 26 publications

FormatFuzzer: Effective Fuzzing of Binary File Formats

FormatFuzzer: Effective Fuzzing of Binary File Formats

Fuzz Testing in Behavior-Based Robotics

FormatFuzzer : Effective Fuzzing of Binary File Formats

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