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Description

Atheris is a Python fuzzing engine guided by coverage, designed to test both Python code and native extensions developed for CPython. It is built on the foundation of libFuzzer, providing an effective method for identifying additional bugs when fuzzing native code. Atheris is compatible with Linux (both 32- and 64-bit) and Mac OS X, supporting Python versions ranging from 3.6 to 3.10. Featuring an integrated libFuzzer, it is well-suited for fuzzing Python applications, but when targeting native extensions, users may need to compile from source to ensure compatibility between the libFuzzer version in Atheris and their Clang installation. Since Atheris depends on libFuzzer, which is a component of Clang, users of Apple Clang will need to install a different version of LLVM, as the default does not include libFuzzer. The implementation of Atheris as a coverage-guided, mutation-based fuzzer (LibFuzzer) simplifies the setup process by eliminating the need for input grammar definition. However, this approach can complicate the generation of inputs for code that processes intricate data structures. Consequently, while Atheris offers ease of use in many scenarios, it may face challenges when dealing with more complex parsing requirements.

Description

Honggfuzz is a software fuzzer focused on enhancing security through its advanced fuzzing techniques. It employs evolutionary and feedback-driven methods that rely on both software and hardware-based code coverage. This tool is designed to operate in a multi-process and multi-threaded environment, allowing users to maximize their CPU's potential without needing to launch multiple fuzzer instances. The file corpus is seamlessly shared and refined across all processes undergoing fuzzing, which greatly enhances efficiency. When persistent fuzzing mode is activated, Honggfuzz exhibits remarkable speed, capable of executing a simple or empty LLVMFuzzerTestOneInput function at an impressive rate of up to one million iterations per second on modern CPUs. It has a proven history of identifying security vulnerabilities, including the notable discovery of the only critical vulnerability in OpenSSL to date. Unlike other fuzzing tools, Honggfuzz can detect and report on hijacked or ignored signals that result from crashes, making it a valuable asset for identifying hidden issues within fuzzed programs. Its robust features make it an essential tool for security researchers aiming to uncover hidden flaws in software systems.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

ClusterFuzz No 
Cygwin No 
FreeBSD No 
Google ClusterFuzz No 
Google OSS-Fuzz Yes 
LibFuzzer Yes 
NetBSD No 
OpenSSL No 
Python Yes 

Integrations

ClusterFuzz Yes 
Cygwin Yes 
FreeBSD Yes 
Google ClusterFuzz Yes 
Google OSS-Fuzz No 
LibFuzzer No 
NetBSD Yes 
OpenSSL Yes 
Python No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App Yes 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Google

Website

github.com/google/atheris

Vendor Details

Company Name

Google

Country

United States

Website

github.com/google/honggfuzz

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Product Features

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