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What is Honggfuzz?

Honggfuzz is a sophisticated software fuzzer dedicated to improving security through its innovative fuzzing methodologies. Utilizing both evolutionary and feedback-driven approaches, it leverages software and hardware-based code coverage for optimal performance. The tool is adept at functioning within multi-process and multi-threaded frameworks, enabling users to fully utilize their CPU capabilities without the need for launching multiple instances of the fuzzer. Sharing and refining the file corpus across all fuzzing processes significantly boosts efficiency. When the persistent fuzzing mode is enabled, Honggfuzz showcases exceptional speed, capable of running a simple or empty LLVMFuzzerTestOneInput function at an astonishing rate of up to one million iterations per second on contemporary CPUs. It has a strong track record of uncovering security vulnerabilities, including the significant identification of the sole critical vulnerability in OpenSSL thus far. In contrast to other fuzzing solutions, Honggfuzz can recognize and report on hijacked or ignored signals resulting from crashes, enhancing its utility in pinpointing obscure issues within fuzzed applications. With its comprehensive features and capabilities, Honggfuzz stands as an invaluable resource for security researchers striving to reveal hidden weaknesses in software architectures. This makes it not only a powerful tool for testing but also a crucial component in the ongoing battle against software vulnerabilities.

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.

Media

Media

Integrations Supported

ClusterFuzz
Cygwin
Firebase
FreeBSD
Google ClusterFuzz
Google OSS-Fuzz
Honggfuzz
Jira
LibFuzzer
NetBSD
OpenSSL
american fuzzy lop

Integrations Supported

ClusterFuzz
Cygwin
Firebase
FreeBSD
Google ClusterFuzz
Google OSS-Fuzz
Honggfuzz
Jira
LibFuzzer
NetBSD
OpenSSL
american fuzzy lop

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Google

Company Location

United States

Company Website

github.com/google/honggfuzz

Company Facts

Organization Name

Google

Company Location

United States

Company Website

google.github.io/clusterfuzz/

Categories and Features

Categories and Features

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