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What is ClusterFuzz?

ClusterFuzz is a sophisticated fuzzing platform aimed at detecting security flaws and stability issues in software applications. Used by Google across its product range, it also functions as the fuzzing backend for OSS-Fuzz. This platform boasts a wide array of features that enable seamless integration of fuzzing into the software development lifecycle. It offers fully automated systems for bug filing, triaging, and resolving issues across various issue trackers. In addition, it accommodates several coverage-guided fuzzing engines to optimize results using methods such as ensemble fuzzing and varied fuzzing techniques. The platform supplies comprehensive statistics that help assess the efficiency of fuzzers and monitor crash rates effectively. With an intuitive web interface, it streamlines management activities and crash investigations, while also supporting multiple authentication options through Firebase. Furthermore, ClusterFuzz enables black-box fuzzing, reduces test case sizes, and implements regression identification via bisection methods, rendering it a thorough solution for software testing. The combination of versatility and reliability found in ClusterFuzz significantly enhances the overall software development experience, making it an invaluable asset.

What is Atheris?

Atheris operates as a fuzzing engine tailored for Python, specifically employing a coverage-guided approach, and it extends its functionality to accommodate native extensions built for CPython. Leveraging libFuzzer as its underlying framework, Atheris proves particularly adept at uncovering additional bugs within native code during fuzzing processes. It is compatible with both 32-bit and 64-bit Linux platforms, as well as Mac OS X, and supports Python versions from 3.6 to 3.10. While Atheris integrates libFuzzer, which makes it well-suited for fuzzing Python applications, users focusing on native extensions might need to compile the tool from its source code to align the libFuzzer version included with Atheris with their installed Clang version. Given that Atheris relies on libFuzzer, which is bundled with Clang, users operating on Apple Clang must install an alternative version of LLVM, as the standard version does not come with libFuzzer. Atheris utilizes a coverage-guided, mutation-based fuzzing strategy, which streamlines the configuration process, eliminating the need for a grammar definition for input generation. However, this approach can lead to complications when generating inputs for code that manages complex data structures. Therefore, users must carefully consider the trade-offs between the simplicity of setup and the challenges associated with handling intricate input types, as these factors can significantly influence the effectiveness of their fuzzing efforts. Ultimately, the decision to use Atheris will hinge on the specific requirements and complexities of the project at hand.

Media

Media

Integrations Supported

Google OSS-Fuzz
LibFuzzer
Firebase
Honggfuzz
Jira
american fuzzy lop

Integrations Supported

Google OSS-Fuzz
LibFuzzer
Python

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

Supported Platforms

Windows
Mac
Linux

Supported Platforms

Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Google

Company Location

United States

Company Website

google.github.io/clusterfuzz/

Company Facts

Organization Name

Google

Company Website

github.com/google/atheris

Categories and Features

Fuzz Testing

Not specified

Categories and Features

Fuzz Testing

Not specified

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