Laszlo Szekeres
László Szekeres is a software security researcher at Google's Software Analysis Team. He develops tools and infrastructure for protecting against security bugs and vulnerabilities, primarily in C/C++ code. His research is focused on automated test generation, program analysis, compiler techniques, and machine learning. He obtained his Ph.D. in Computer Science from Stony Brook University in 2017. In 2010 he was awarded with the Fulbright Foreign Student Scholarship. Before returning to academia for his doctorate degree, he led a security research and evaluation team at a spin-off company of the Budapest University of Technology and Economics. More information at lszekeres.com.
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On the Reliability of Coverage-Based Fuzzer Benchmarking
Marcel Böhme
Proceedings of the 44th International Conference on Software Engineering (ICSE'22), IEEE (2022)
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Given a program where none of our fuzzers finds any bugs, how do we know which fuzzer is better? In practice, we often look to code coverage as a proxy measure of fuzzer effectiveness and consider the fuzzer which achieves more coverage as the better one.
Indeed, evaluating 10 fuzzers for 23 hours on 24 programs, we find that a fuzzer that covers more code also finds more bugs. There is a very strong correlation between the coverage achieved and the number of bugs found by a fuzzer. Hence, it might seem reasonable to compare fuzzers in terms of coverage achieved, and from that derive empirical claims about a fuzzer’s superiority at finding bugs.
Curiously enough, however, we find no strong agreement on which fuzzer is superior if we compared multiple fuzzers in terms of coverage achieved instead of the number of bugs found. The fuzzer best at achieving coverage, may not be best at finding bugs.
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FuzzBench: An Open Fuzzer Benchmarking Platform and Service
Laurent Maurice Romain Simon
Read Trevelin Sprabery
Abhishek Arya
Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Association for Computing Machinery, New York, NY, USA (2021)
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Fuzzing is a key tool used to reduce bugs in production software. At Google, fuzzing has uncovered tens of thousands of bugs. Fuzzing is also a popular subject of academic research. In 2020 alone, over 120 papers were published on the topic of improving, developing, and evaluating fuzzers and fuzzing techniques. Yet, proper evaluation of fuzzing techniques remains elusive. The community has struggled to converge on methodology and standard tools for fuzzer evaluation.
To address this problem, we introduce FuzzBench as an open-source turnkey platform and free service for evaluating fuzzers. It aims to be easy to use, fast, reliable, and provides reproducible experiments. Since its release in March 2020, Fuzzbench has been widely used both in industry and academia, carrying out more than 150 experiments for external users. It has been used by several published and in-the-work papers from academic groups, and has had real impact on the most widely used fuzzing tools in industry. The presented case studies suggest that FuzzBench is on its way to becoming a standard fuzzer benchmarking platform.
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FUDGE: Fuzz Driver Generation at Scale
Yaohui Chen
Markus Kusano
Caroline Lemieux
Wei Wang
Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM
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At Google we have found tens of thousands of security and robustness bugs by fuzzing C and C++ libraries. To fuzz a library, a fuzzer requires a fuzz driver—which exercises some library code—to which it can pass inputs. Unfortunately, writing fuzz drivers remains a primarily manual exercise, a major hindrance to the widespread adoption of fuzzing. In this paper, we address this major hindrance by introducing the Fudge system for automated fuzz driver generation. Fudge automatically generates fuzz driver candidates for libraries based on existing client code. We have used Fudge to generate thousands of new drivers for a wide variety of libraries. Each generated driver includes a synthesized C/C++ program and a corresponding build script, and is automatically analyzed for quality. Developers have integrated over 200 of these generated drivers into continuous fuzzing services and have committed to address reported security bugs. Further, several of these fuzz drivers have been upstreamed to open source projects and integrated into the OSS-Fuzz fuzzing infrastructure. Running these fuzz drivers has resulted in over 150 bug fixes, including the elimination of numerous exploitable security vulnerabilities.
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