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What is american fuzzy lop?

American Fuzzy Lop, known as afl-fuzz, is a security-oriented fuzzer that employs a novel method of compile-time instrumentation combined with genetic algorithms to automatically create effective test cases, which can reveal hidden internal states within the binary under examination. This technique greatly improves the functional coverage of the fuzzed code. Moreover, the streamlined and synthesized test cases generated by this tool can prove invaluable for kickstarting other, more intensive testing methodologies later on. In contrast to numerous other instrumented fuzzers, afl-fuzz prioritizes practicality by maintaining minimal performance overhead while utilizing a wide range of effective fuzzing strategies that reduce the necessary effort. It is designed to require minimal setup and can seamlessly handle complex, real-world scenarios typical of image parsing or file compression libraries. As an instrumentation-driven genetic fuzzer, it excels at crafting intricate file semantics that are applicable to a broad spectrum of difficult targets, making it an adaptable option for security assessments. Additionally, its capability to adjust to various environments makes it an even more attractive choice for developers in pursuit of reliable solutions. This versatility ensures that afl-fuzz remains a valuable asset in the ongoing quest for software security.

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

C
C++
ClusterFuzz
Firebase
FreeBSD
Go
Google ClusterFuzz
Honggfuzz
Java
Jira
LibFuzzer
NetBSD
OCaml
Objective-C
OpenBSD
Python
QEMU
Rust
american fuzzy lop

Integrations Supported

C
C++
ClusterFuzz
Firebase
FreeBSD
Go
Google ClusterFuzz
Honggfuzz
Java
Jira
LibFuzzer
NetBSD
OCaml
Objective-C
OpenBSD
Python
QEMU
Rust
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

Google

Company Location

United States

Company Website

github.com/google/AFL

Company Facts

Organization Name

Google

Company Website

github.com/google/clusterfuzz

Categories and Features

Categories and Features

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