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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 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.

Media

Media

Integrations Supported

ClusterFuzz
FreeBSD
Google ClusterFuzz
NetBSD
C
C++
Cygwin
Go
Java
OCaml
Objective-C
OpenBSD
OpenSSL
Python
QEMU
Rust

Integrations Supported

ClusterFuzz
FreeBSD
Google ClusterFuzz
NetBSD
C
C++
Cygwin
Go
Java
OCaml
Objective-C
OpenBSD
OpenSSL
Python
QEMU
Rust

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 Location

United States

Company Website

github.com/google/honggfuzz

Categories and Features

Categories and Features

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