Best Fuzz Testing Tools for LibFuzzer
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Compare the Top Fuzz Testing Tools that integrate with LibFuzzer as of September 2025
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This a list of Fuzz Testing tools that integrate with LibFuzzer. Use the filters on the left to add additional filters for products that have integrations with LibFuzzer. View the products that work with LibFuzzer in the table below.
What are Fuzz Testing Tools for LibFuzzer?
Fuzz testing tools are automated software tools used to detect bugs and vulnerabilities in computer systems. They generate large amounts of random input data to test the robustness of a system. These tools are commonly used in software development to enhance the quality and security of a product. Fuzz testing tools can be applied to various types of systems, including web applications, mobile apps, and operating systems. They have become an essential part of the testing process in modern software development due to their ability to uncover hidden flaws that traditional testing methods may miss. Compare and read user reviews of the best Fuzz Testing tools for LibFuzzer currently available using the table below. This list is updated regularly.
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1
Fuzzbuzz
Fuzzbuzz
The Fuzzbuzz workflow is very similar to other CI/CD testing workflows. However, unlike other testing workflows, fuzz testing requires multiple jobs to run simultaneously, which results in a few extra steps. Fuzzbuzz is a fuzz testing platform. We make it trivial for developers to add fuzz tests to their code and run them in CI/CD, helping them catch critical bugs and vulnerabilities before they hit production. Fuzzbuzz completely integrates into your environment, following you from the terminal to CI/CD. Write a fuzz test in your environment and use your own IDE, terminal, or build tools. Push to CI/CD and Fuzzbuzz will automatically start running your fuzz tests against your latest code changes. Get notified when bugs are found through Slack, GitHub, or email. Catch regressions as new changes are automatically tested and compared to previous runs. Code is built and instrumented by Fuzzbuzz as soon as a change is detected.Starting Price: Free -
2
Jazzer
Code Intelligence
Jazzer is a coverage-guided, in-process fuzzer for the JVM platform developed by Code Intelligence. It is based on libFuzzer and brings many of its instrumentation-powered mutation features to the JVM. You can use Docker to try out Jazzer's autofuzz mode, which automatically generates arguments to a given Java function and reports unexpected exceptions and detected security issues. You can also use GitHub release archives to run a standalone Jazzer binary that starts its own JVM configured for fuzzing.Starting Price: Free -
3
Google ClusterFuzz
Google
ClusterFuzz is a scalable fuzzing infrastructure that finds security and stability issues in software. Google uses ClusterFuzz to fuzz all Google products and as the fuzzing backend for OSS-Fuzz. ClusterFuzz provides many features to seamlessly integrate fuzzing into a software project’s development process. Fully automatic bug filing, triage, and closing for various issue trackers. Supports multiple coverages guided fuzzing engines for optimal results (with ensemble fuzzing and fuzzing strategies). Statistics for analyzing fuzzer performance, and crash rates. Easy to use web interface for management and viewing crashes. Support for various authentication providers using Firebase. Support for black-box fuzzing, test case minimization, and regression finding through bisection.Starting Price: Free -
4
Atheris
Google
Atheris is a coverage-guided Python fuzzing engine. It supports fuzzing of Python code, but also native extensions written for CPython. Atheris is based on libFuzzer. When fuzzing native code, Atheris can be used to catch extra bugs. Atheris supports Linux (32- and 64-bit) and Mac OS X, with Python versions 3.6-3.10. It comes with a built-in libFuzzer, which is fine for fuzzing Python code. If you plan to fuzz native extensions, you may need to build from source to ensure the libFuzzer version in Atheris matches your Clang version. Atheris relies on libFuzzer, which is distributed with Clang. Apple Clang doesn't come with libFuzzer, so you'll need to install a new version of LLVM. Atheris is based on a coverage-guided mutation-based fuzzer (LibFuzzer). This has the advantage of not requiring any grammar definition for generating inputs, making its setup easier. The disadvantage is that it will be harder for the fuzzer to generate inputs for code that parses complex data types.Starting Price: Free -
5
ClusterFuzz
Google
ClusterFuzz is a scalable fuzzing infrastructure that finds security and stability issues in software. Google uses ClusterFuzz to fuzz all Google products and as the fuzzing backend for OSS-Fuzz. ClusterFuzz provides many features to seamlessly integrate fuzzing into a software project’s development process. Fully automatic bug filing, triage, and closing for various issue trackers. Supports multiple coverages guided fuzzing engines for optimal results (with ensemble fuzzing and fuzzing strategies). Statistics for analyzing fuzzer performance, and crash rates. Easy to use web interface for management and viewing crashes. Support for various authentication providers using Firebase. Support for black-box fuzzing, test case minimization, and regression finding through bisection.
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