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Description
ClusterFuzz is an advanced fuzzing platform designed to identify security vulnerabilities and stability problems within software applications. Utilized by Google for all its products, it also serves as the fuzzing backend for OSS-Fuzz. This infrastructure offers a plethora of features that facilitate the integration of fuzzing into the development lifecycle of software projects. It includes fully automated processes for bug filing, triage, and resolution across different issue trackers. Moreover, it supports various coverage-guided fuzzing engines to achieve optimal outcomes through techniques like ensemble fuzzing and diverse fuzzing strategies. The platform provides detailed statistics for evaluating fuzzer efficiency and tracking crash rates. Its user-friendly web interface simplifies management tasks and crash examinations, while it also accommodates multiple authentication providers via Firebase. Additionally, ClusterFuzz supports black-box fuzzing, minimizes test cases, and employs regression identification through bisection techniques, making it a comprehensive solution for software testing. The versatility and robustness of ClusterFuzz truly enhance the software development process.
Description
LibFuzzer serves as an in-process, coverage-guided engine for evolutionary fuzzing. By being linked directly with the library under examination, it injects fuzzed inputs through a designated entry point, or target function, allowing it to monitor the code paths that are executed while creating variations of the input data to enhance code coverage. The coverage data is obtained through LLVM’s SanitizerCoverage instrumentation, ensuring that users have detailed insights into the testing process. Notably, LibFuzzer continues to receive support, with critical bugs addressed as they arise. To begin utilizing LibFuzzer with a library, one must first create a fuzz target—this function receives a byte array and interacts with the API being tested in a meaningful way. Importantly, this fuzz target operates independently of LibFuzzer, which facilitates its use alongside other fuzzing tools such as AFL or Radamsa, thereby providing versatility in testing strategies. Furthermore, the ability to leverage multiple fuzzing engines can lead to more robust testing outcomes and clearer insights into the library's vulnerabilities.
API Access
Has API
API Access
Has API
Integrations
Atheris
C
C++
ClusterFuzz
Firebase
Fuzzbuzz
Google ClusterFuzz
Google OSS-Fuzz
Honggfuzz
Jazzer
Integrations
Atheris
C
C++
ClusterFuzz
Firebase
Fuzzbuzz
Google ClusterFuzz
Google OSS-Fuzz
Honggfuzz
Jazzer
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Country
United States
Website
google.github.io/clusterfuzz/
Vendor Details
Company Name
LLVM Project
Founded
2003
Website
llvm.org/docs/LibFuzzer.html