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What is LibFuzzer?

LibFuzzer is an in-process engine that employs coverage-guided techniques for evolutionary fuzzing. By integrating directly with the library being tested, it injects generated fuzzed inputs into a specific entry point or target function, allowing it to track executed code paths while modifying the input data to improve code coverage. The coverage information is gathered through LLVM’s SanitizerCoverage instrumentation, which provides users with comprehensive insights into the testing process. Importantly, LibFuzzer is continuously maintained, with critical bugs being resolved as they are identified. To use LibFuzzer with a particular library, the first step is to develop a fuzz target; this function takes a byte array and interacts meaningfully with the API under scrutiny. Notably, this fuzz target functions independently of LibFuzzer, making it compatible with other fuzzing tools like AFL or Radamsa, which adds flexibility to testing approaches. Moreover, combining various fuzzing engines can yield more thorough testing results and deeper understanding of the library's security flaws, ultimately enhancing the overall quality of the code. The ongoing evolution of fuzzing techniques ensures that developers are better equipped to identify and address potential vulnerabilities effectively.

What is Google ClusterFuzz?

ClusterFuzz is a comprehensive fuzzing framework aimed at identifying security weaknesses and stability issues within software applications. Used extensively by Google, it serves as the testing backbone for all its products and functions as the fuzzing engine for OSS-Fuzz. This powerful infrastructure comes equipped with numerous features that enable the seamless integration of fuzzing into the software development process. It offers fully automated procedures for filing bugs, triaging them, and resolving issues across various issue tracking platforms. Supporting multiple coverage-guided fuzzing engines, it enhances outcomes through ensemble fuzzing and a range of fuzzing techniques. Moreover, the system provides statistical data to evaluate the effectiveness of fuzzers and track the frequency of crashes. Users benefit from a user-friendly web interface that streamlines the management of fuzzing tasks and crash analysis. ClusterFuzz also accommodates various authentication methods via Firebase, and it boasts functionalities for black-box fuzzing, reducing test cases, and pinpointing regressions through bisection. In conclusion, this powerful tool not only elevates software quality and security but also becomes an essential asset for developers aiming to refine their applications, ultimately leading to more robust and reliable software solutions.

Media

Media

Integrations Supported

Atheris
C
C++
ClusterFuzz
Firebase
Fuzzbuzz
Google ClusterFuzz
Honggfuzz
Jazzer
Jira
LibFuzzer
american fuzzy lop

Integrations Supported

Atheris
C
C++
ClusterFuzz
Firebase
Fuzzbuzz
Google ClusterFuzz
Honggfuzz
Jazzer
Jira
LibFuzzer
american fuzzy lop

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Free
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

LLVM Project

Date Founded

2003

Company Website

llvm.org/docs/LibFuzzer.html

Company Facts

Organization Name

Google

Company Website

github.com/google/clusterfuzz

Categories and Features

Categories and Features

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