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What is afl-unicorn?

AFL-Unicorn enables the fuzzing of any binary that can be emulated with the Unicorn Engine, providing the ability to focus on specific code segments during testing. As long as the desired code can be emulated using the Unicorn Engine, AFL-Unicorn can be utilized effectively for fuzzing tasks. The Unicorn Mode features block-edge instrumentation akin to AFL's QEMU mode, allowing AFL to collect block coverage data from the emulated code segments, which is essential for its input generation process. This functionality is contingent upon the meticulous configuration of a Unicorn-based test harness, which plays a crucial role in loading the intended code, setting up the initial state, and integrating data altered by AFL from its storage. Once these parameters are established, the test harness simulates the target binary code, and upon detecting a crash or error, it sends a signal to indicate the problem. Although this framework has been primarily validated on Ubuntu 16.04 LTS, it is built to work seamlessly with any operating system that can support both AFL and Unicorn. By utilizing this framework, developers can significantly enhance their fuzzing strategies and streamline their binary analysis processes, leading to more effective vulnerability detection and software reliability improvements. This broader compatibility opens up new opportunities for developers to adopt advanced fuzzing techniques across various platforms.

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

Firebase
Honggfuzz
Jira
LibFuzzer
american fuzzy lop

Integrations Supported

Firebase
Honggfuzz
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

Battelle

Company Website

github.com/Battelle/afl-unicorn

Company Facts

Organization Name

Google

Company Website

github.com/google/clusterfuzz

Categories and Features

Categories and Features

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