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检索条件"主题词=coverage-guided fuzzing"

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So Many Fuzzers, So Little Time✱: Experience from Evaluating Fuzzers on the Contiki-NG Network (Hay)Stack 22

So Many Fuzzers, So Little Time✱: Experience from Evaluatin...

引用

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： Clement Poncelet Konstantinos Sagonas Nicolas Tsiftes Department of Information Technology Uppsala University Sweden Department of Information Technology Uppsala University Sweden and School of Electrical and Computer Engineering National Technical University of Athens Greece RISE Research Institutes of Sweden Sweden

ISBN: (纸本)9781450394758

Fuzz testing (“fuzzing”) is a widely-used and effective dynamic technique to discover crashes and security vulnerabilities in software, supported by numerous tools, which keep improving in terms of their detection capabilities and speed of execution. In this paper, we report our findings from using state-of-the-art mutation-based and hybrid fuzzers (AFL, Angora, Honggfuzz, Intriguer, MOpt-AFL, QSym, and SymCC) on a non-trivial code base, that of Contiki-NG, to expose and fix serious vulnerabilities in various layers of its network stack, during a period of more than three years. As a by-product, we provide a Git-based platform which allowed us to create and apply a new, quite challenging, open-source bug suite for evaluating fuzzers on real-world software vulnerabilities. Using this bug suite, we present an impartial and extensive evaluation of the effectiveness of these fuzzers, and measure the impact that sanitizers have on it. Finally, we offer our experiences and opinions on how fuzzing tools should be used and evaluated in the future.

关键词： IoT security testing coverage-guided fuzzing Software security fuzz testing Contiki-NG hybrid fuzzing

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CRFuzz: fuzzing Multi-purpose Programs through Input Validation 2020

CRFuzz: Fuzzing Multi-purpose Programs through Input Validat...

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28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Song, Suhwan Song, Chengyu Jang, Yeongjin Lee, Byoungyoung Seoul Natl Univ Seoul South Korea Univ Calif Riverside Riverside CA 92521 USA Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781450370431

Fuzz testing has been proved its effectiveness in discovering software vulnerabilities. Empowered its randomness nature along with a coverage-guiding feature, fuzzing has been identified a vast number of vulnerabilities in real-world programs. This paper begins with an observation that the design of the current state-of-the-art fuzzers is not well suited for a particular (but yet important) set of software programs. Specifically, current fuzzers have limitations in fuzzing programs serving multiple purposes, where each purpose is controlled by extra options. This paper proposes CRFuzz, which overcomes this limitation. CRFuzz designs a clustering analysis to automatically predict if a newly given input would be accepted or not by a target program. Exploiting this prediction capability, CRFuzz is designed to efficiently explore the programs with multiple purposes. We employed CRFuzz for three state-of-the-art fuzzers, AFL, QSYM, and MOpt, and CRFuzz-augmented versions have shown 19.3% and 5.68% better path and edge coverage on average. More importantly, during two weeks of long-running experiments, CRFuzz discovered 277 previously unknown vulnerabilities where 212 of those are already confirmed and fixed by the respected vendors. We would like to emphasize that many of these vulnerabilities were discoverd from FFMpeg, ImageMagick, and Graphic smagick, all of which are targets of Google's OSS-Fuzz project and thus heavily fuzzed for last three years by far. Nevertheless, CRFuzz identified a remarkable number of vulnerabilities, demonstrating its effectiveness of vulnerability finding capability.

关键词： Fuzz testing coverage-guided fuzzing

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Zeror: Speed Up fuzzing with coverage-sensitive Tracing and Scheduling 35

Zeror: Speed Up Fuzzing with Coverage-sensitive Tracing and ...

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35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Zhou, Chijin Wang, Mingzhe Liang, Jie Liu, Zhe Jiang, Yu Tsinghua Univ Sch Software KLISS BNRist Beijing Peoples R China NUAA Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781450367684

coverage-guided fuzzing is one of the most popular software testing techniques for vulnerability detection. While effective, current fuzzing methods suffer from significant performance penalty due to instrumentation overhead, which limits its practical use. Existing solutions improve the fuzzing speed by decreasing instrumentation overheads but sacrificing coverage accuracy, which results in unstable performance of vulnerability detection. In this paper, we propose a coverage-sensitive tracing and scheduling framework Zeror that can improve the performance of existing fuzzers, especially in their speed and vulnerability detection. The Zeror is mainly made up of two parts: (1) a self-modifying tracing mechanism to provide a zero-overhead instrumentation for more effective coverage collection, and (2) a real-time scheduling mechanism to support adaptive switch between the zero-overhead instrumented binary and the fully instrumented binary for better vulnerability detection. In this way, Zeror is able to decrease collection overhead and preserve fine-grained coverage for guidance. For evaluation, we implement a prototype of Zeror and evaluate it on Google fuzzer-test-suite, which consists of 24 widely-used applications. The results show that Zeror performs better than existing fuzzing speed-up frameworks such as Untracer and INSTRIM, improves the execution speed of the state-of-the-art fuzzers such as AFL and MOPT by 159.80%, helps them achieve better coverage (averagely 10.14% for AFL, 6.91% for MOPT) and detect vulnerabilities faster (averagely 29.00% for AFL, 46.99% for MOPT).

关键词： coverage-guided fuzzing coverage-Sensitive Tracing Scheduling

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LLVM-based Hybrid fuzzing with LibKluzzer (Competition Contribution) 23rd

LLVM-based Hybrid Fuzzing with LibKluzzer (Competition Contr...

引用

23rd International Conference on Fundamental Approaches to Software Engineering (FASE) held as part of the Joint European Conferences on Theory and Practice of Software (ETAPS)

作者： Le, Hoang M. Univ Bremen Inst Comp Sci Bremen Germany

ISBN: (纸本)9783030452346;9783030452339

LibKluzzer is a novel implementation of hybrid fuzzing, which combines the strengths of coverage-guided fuzzing and dynamic symbolic execution (a.k.a. whitebox fuzzing). While coverage-guided fuzzing can discover new execution paths at nearly native speed, whitebox fuzzing is capable of getting through complex branch conditions. In contrast to existing hybrid fuzzers, that operate directly on binaries, LibKluzzer leverages the LLVM compiler framework to work at the source code level. It employs LibFuzzer as the coverage-guided fuzzing component and KLUZZER, an extension of KLEE, as the whitebox fuzzing component.

关键词： Hybrid fuzzing coverage-guided fuzzing Symbolic Execution LLVM

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Zeror: speed up fuzzing with coverage-sensitive tracing and scheduling 20

Zeror: speed up fuzzing with coverage-sensitive tracing and ...

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Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

作者： Chijin Zhou Mingzhe Wang Jie Liang Zhe Liu Yu Jiang Tsinghua University Beijing China NUAA Nanjing China

ISBN: (纸本)9781450367684

coverage-guided fuzzing is one of the most popular software testing techniques for vulnerability detection. While effective, current fuzzing methods suffer from significant performance penalty due to instrumentation overhead, which limits its practical use. Existing solutions improve the fuzzing speed by decreasing instrumentation overheads but sacrificing coverage accuracy, which results in unstable performance of vulnerability *** this paper, we propose a coverage-sensitive tracing and scheduling framework Zeror that can improve the performance of existing fuzzers, especially in their speed and vulnerability detection. The Zeror is mainly made up of two parts: (1) a self-modifying tracing mechanism to provide a zero-overhead instrumentation for more effective coverage collection, and (2) a real-time scheduling mechanism to support adaptive switch between the zero-overhead instrumented binary and the fully instrumented binary for better vulnerability detection. In this way, Zeror is able to decrease collection overhead and preserve fine-grained coverage for *** evaluation, we implement a prototype of Zeror and evaluate it on Google fuzzer-test-suite, which consists of 24 widely-used applications. The results show that Zeror performs better than existing fuzzing speed-up frameworks such as Untracer and INSTRIM, improves the execution speed of the state-of-the-art fuzzers such as AFL and MOPT by 159.80%, helps them achieve better coverage (averagely 10.14% for AFL, 6.91% for MOPT) and detect vulnerabilities faster (averagely 29.00% for AFL, 46.99% for MOPT).

关键词： coverage-sensitive tracing scheduling coverage-guided fuzzing

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SpeedNeuzz: Speed Up Neural Program Approximation with Neighbor Edge Knowledge 19

SpeedNeuzz: Speed Up Neural Program Approximation with Neigh...

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19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Li, Yi Xiao, Xi Zhu, Xiaogang Chen, Xiao Wen, Sheng Zhang, Bin Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia Monash Univ Fac Informat Technol Melbourne Vic Australia

ISBN: (纸本)9781665403924

fuzzing has been a great success in discovering real-world complex programs vulnerabilities. However, fuzzing achieves this effect by blindly generating a large number of test cases, which undoubtedly contains a lot of meaningless mutation inputs. To solve the blindness, machine learning technology is applied to fuzzing in recent work. Some of the machine learning based methods focus on locating and mutating the key bytes in the input, but they do not pay attention to the characteristics in the field of fuzzing when they combine machine learning technology with fuzzing. In this paper, we implement a new fuzzer, called SpeedNeuzz, which uses neural networks to model the branch behaviours of the program based on accurate training data after mitigating the hash collision of AFL. Furthermore, SpeedNeuzz locates and mutates critical bytes in the program input with a gradient-based strategy as well as neighbor edge information. Taking the neighbor edge knowledge into account, we can further reduce the blindness of the mutation based on gradient information so that SpeedNeuzz can generate a large number of quality inputs. Experiments on several real-world programs prove that SpeedNeuzz can achieve higher edge coverage than the state-of-the-art fuzzer NEUZZ under the same time budget.

关键词： coverage-guided fuzzing Neural Network Program Smoothing

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JQF: coverage-guided Property-Based Testing in Java 2019

JQF: Coverage-Guided Property-Based Testing in Java

引用

28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

作者： Padhye, Rohan Lemieux, Caroline Sen, Koushik Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450362245

We present JQF, a platform for performing coverage-guided fuzz testing in Java. JQF is designed both for practitioners,who wish to find bugs in Java programs, as well as for researchers,who wish to implement new fuzzing algorithms. Practitioners write QuickCheck-style test methods that take inputs as formal parameters. JQF instruments the test program's bytecode and continuously executes tests using inputs that are generated in a coverage-guided fuzzing loop. JQF's input-generation mechanism is extensible. Researchers can implement custom fuzzing algorithms by extending JQF's Guidance interface. A Guidance instance responds to code coverage events generated during the execution of a test case, such as function calls and conditional jumps, and provides the next input. We describe several guidances that currently ship with JQF, such as: semantic fuzzing with Zest, binary fuzzing with AFL, and complexity fuzzing with Perffuzz. JQF is a mature tool that is open-source and publicly available. At the time of writing, JQF has been successful in discovering 42 previously unknown bugs in widely used open-source software such as OpenJDK, Apache Commons, and the Google Closure Compiler.

关键词： coverage-guided fuzzing property-based testing QuickCheck

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DeepHunter: A coverage-guided Fuzz Testing Framework for Deep Neural Networks 2019

DeepHunter: A Coverage-Guided Fuzz Testing Framework for Dee...

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28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

作者： Xie, Xiaofei Ma, Lei Juefei-Xu, Felix Xue, Minhui Chen, Hongxu Liu, Yang Zhao, Jianjun Li, Bo Yin, Jianxiong See, Simon Nanyang Technol Univ Singapore Singapore Kyushu Univ Fukuoka Fukuoka Japan Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Adelaide Adelaide SA Australia Zhejiang Sci Tech Univ Hangzhou Zhejiang Peoples R China Univ Illinois Urbana IL USA NVIDIA AI Tech Ctr Singapore Singapore

ISBN: (纸本)9781450362245

The past decade has seen the great potential of applying deep neural network (DNN) based software to safety-critical scenarios, such as autonomous driving. Similar to traditional software, DNNs could exhibit incorrect behaviors, caused by hidden defects, leading to severe accidents and losses. In this paper, we propose DeepHunter, a coverage-guided fuzz testing framework for detecting potential defects of general-purpose DNNs. To this end, we first propose a metamorphic mutation strategy to generate new semantically preserved tests, and leverage multiple extensible coverage criteria as feedback to guide the test generation. We further propose a seed selection strategy that combines both diversity-based and recency-based seed selection. We implement and incorporate 5 existing testing criteria and 4 seed selection strategies in DeepHunter. Large-scale experiments demonstrate that (1) our metamorphic mutation strategy is useful to generate new valid tests with the same semantics as the original seed, by up to a 98% validity ratio;(2) the diversity-based seed selection generally weighs more than recency-based seed selection in boosting the coverage and in detecting defects;(3) DeepHunter outperforms the state of the arts by coverage as well as the quantity and diversity of defects identified;(4) guided by corner-region based criteria, DeepHunteris useful to capture defects during the DNN quantization for platform migration.

关键词： Deep learning testing metamorphic testing coverage-guided fuzzing

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