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Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2019
To be effective, software test generation needs to well cover the space of possible inputs. Traditional fuzzing generates large numbers of random inputs, which however are unlikely to contain keywords and other specific inputs of non-trivial input languages.
Mathis, Björn +5 more
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To be effective, software test generation needs to well cover the space of possible inputs. Traditional fuzzing generates large numbers of random inputs, which however are unlikely to contain keywords and other specific inputs of non-trivial input languages.
Mathis, Björn +5 more
openaire +1 more source
International Conference on Software Engineering
Bugs in Deep Learning (DL) libraries may affect almost all downstream DL applications, and it is crucial to ensure the quality of such systems. It is challenging to generate valid input programs for fuzzing DL libraries, since the input programs need to ...
Yinlin Deng +5 more
semanticscholar +1 more source
Bugs in Deep Learning (DL) libraries may affect almost all downstream DL applications, and it is crucial to ensure the quality of such systems. It is challenging to generate valid input programs for fuzzing DL libraries, since the input programs need to ...
Yinlin Deng +5 more
semanticscholar +1 more source
Communications of the ACM, 2020
Reviewing software testing techniques for finding security vulnerabilities.
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Reviewing software testing techniques for finding security vulnerabilities.
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T-Fuzz: Fuzzing by Program Transformation
2018 IEEE Symposium on Security and Privacy (SP), 2018Fuzzing is a simple yet effective approach to discover software bugs utilizing randomly generated inputs. However, it is limited by coverage and cannot find bugs hidden in deep execution paths of the program because the randomly generated inputs fail complex sanity checks, e.g., checks on magic values, checksums, or hashes.
Hui Peng +2 more
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Low-Cost and Comprehensive Non-textual Input Fuzzing with LLM-Synthesized Input Generators
USENIX Security SymposiumModern software often accepts inputs with highly complex grammars. Recent advances in large language models (LLMs) have shown that they can be used to synthesize high-quality natural language text and code that conforms to the grammar of a given input ...
Kunpeng Zhang +4 more
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LLMIF: Augmented Large Language Model for Fuzzing IoT Devices
IEEE Symposium on Security and PrivacyDespite the efficacy of fuzzing in verifying the implementation correctness of network protocols, existing IoT protocol fuzzing approaches grapple with several limitations, including obfuscated message formats, unresolved message dependencies, and a lack
Jincheng Wang, Le Yu, Xiapu Luo
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Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source
International Conference on Software Engineering, 2022Deep learning (DL) systems can make our life much easier, and thus are gaining more and more attention from both academia and industry. Meanwhile, bugs in DL systems can be disastrous, and can even threaten human lives in safety-critical applications. To
Anjiang Wei +3 more
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Fuzzing BusyBox: Leveraging LLM and Crash Reuse for Embedded Bug Unearthing
USENIX Security SymposiumBusyBox, an open-source software bundling over 300 essential Linux commands into a single executable, is ubiquitous in Linux-based embedded devices. Vulnerabilities in BusyBox can have far-reaching consequences, affecting a wide array of devices.
Asmita +5 more
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FUZZING TESTING. CLASSIFICATION OF MODERN FUZZING TOOLS
Сборник избранных статей по материалам научных конференций ГНИИ "Нацразвитие" (Санкт-Петербург, Август 2021), 2021В статье приводятся определение фаззинг тестирования, основные этапы фаззинга, рассматривается классификация фаззеров. The article describes the definition of fuzzing testing, the main stages of fuzzing, and discusses the classification of fuzzers.
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The Mutators Reloaded: Fuzzing Compilers with Large Language Model Generated Mutation Operators
International Conference on Architectural Support for Programming Languages and Operating SystemsCrafting high-quality mutators-the core of mutation-based fuzzing that shapes the search space-is challenging. It requires human expertise and creativity, and their implementation demands knowledge of compiler internals.
Xianfei Ou +3 more
semanticscholar +1 more source