Compiler fuzzing: how much does it matter?

Despite much recent interest in randomised testing (fuzzing) of compilers, the practical impact of fuzzer-found compiler bugs on real-world applications has barely been assessed. We present the first quantitative and qualitative study of the tangible impact of miscompilation bugs in a mature compiler. We follow a rigorous methodology where the bug impact over the compiled application is evaluated based on (1) whether the bug appears to trigger during compilation; (2) the extent to which generated assembly code changes syntactically due to triggering of the bug; and (3) whether such changes cause regression test suite failures, or whether we can manually find application inputs that trigger execution divergence due to such changes. The study is conducted with respect to the compilation of more than 10 million lines of C/C++ code from 309 Debian packages, using 12% of the historical and now fixed miscompilation bugs found by four state-of-the-art fuzzers in the Clang/LLVM compiler, as well as 18 bugs found by human users compiling real code or as a by-product of formal verification efforts. The results show that almost half of the fuzzer-found bugs propagate to the generated binaries for at least one package, in which case only a very small part of the binary is typically affected, yet causing two failures when running the test suites of all the impacted packages. User-reported and formal verification bugs do not exhibit a higher impact, with a lower rate of triggered bugs and one test failure. The manual analysis of a selection of the syntactic changes caused by some of our bugs (fuzzer-found and non fuzzer-found) in package assembly code, shows that either these changes have no semantic impact or that they would require very specific runtime circumstances to trigger execution divergence

Revisiting Neural Program Smoothing for Fuzzing

Testing with randomly generated inputs (fuzzing) has gained significant traction due to its capacity to expose program vulnerabilities automatically. Fuzz testing campaigns generate large amounts of data, making them ideal for the application of machine learning (ML). Neural program smoothing, a specific family of ML-guided fuzzers, aims to use a neural network as a smooth approximation of the program target for new test case generation. In this paper, we conduct the most extensive evaluation of neural program smoothing (NPS) fuzzers against standard gray-box fuzzers (>11 CPU years and >5.5 GPU years), and make the following contributions: (1) We find that the original performance claims for NPS fuzzers do not hold; a gap we relate to fundamental, implementation, and experimental limitations of prior works. (2) We contribute the first in-depth analysis of the contribution of machine learning and gradient-based mutations in NPS . (3) We implement Neuzz++, which shows that addressing the practical limitations of NPS fuzzers improves performance, but standard gray-box fuzzers almost always surpass NPS-based fuzzers. (4) As a consequence, we propose new guidelines targeted at benchmarking fuzzing based on machine learning, and present a platform, MLFuzz, with GPU access for easy and reproducible evaluation of ML -based fuzzers. Neuzz++, MLFuzz, and all our data are public

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 24th International Conference on Fundamental Approaches to Software Engineering, FASE 2021, which took place during March 27–April 1, 2021, and was held as part of the Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg but changed to an online format due to the COVID-19 pandemic. The 16 full papers presented in this volume were carefully reviewed and selected from 52 submissions. The book also contains 4 Test-Comp contributions

Concolic Execution Tailored for Hybrid Fuzzing

Recently, hybrid fuzzing, which combines fuzzing and concolic execution, has been highlighted to overcome limitations of both techniques. Despite its success in contrived programs such as DARPA Cyber Grand Challenge (CGC), it still falls short in finding bugs in real-world software due to its low performance of existing concolic executors. To address this issue, this dissertation suggests and demonstrates concolic execution tailored for hybrid fuzzing with two systems: QSYM and Hybridra. First, we present QSYM, a binary-only concolic executor tailored for hybrid fuzzing. It significantly improves the performance of conventional concolic executors by removing redundant symbolic emulations for a binary. Moreover, to efficiently produce test cases for fuzzing, even sacrificing its soundness, QSYM introduces two key techniques: optimistic solving and basic block pruning. As a result, QSYM outperforms state-of-the-art fuzzers, and, more importantly, it found 13 new bugs in eight real-world programs, including file, ffmpeg, and OpenJPEG. Enhancing the key idea of QSYM, we discuss Hybridra, a new concolic executor for file systems. To apply hybrid fuzzing for file systems, which are gigantic and convoluted, Hybridra employs compilation-based concolic execution to boost concolic execution leveraging the existing of source code. Moreover, Hybridra introduces a new technique called staged reduction, which combines existing heuristics to efficiently generate test cases for file systems. Consequently, Hybridra outperforms a state-of-the-art file system fuzzer, Hydra, by achieving higher code coverage, and successfully discovered four new bugs in btrfs, which has been heavily tested by other fuzzers.Ph.D