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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 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.

Media

Media

Integrations Supported

Atheris
C
C++
ClusterFuzz
Fuzzbuzz
Google ClusterFuzz
Jazzer

Integrations Supported

Atheris
C
C++
ClusterFuzz
Fuzzbuzz
Google ClusterFuzz
Jazzer

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

LLVM Project

Date Founded

2003

Company Website

llvm.org/docs/LibFuzzer.html

Categories and Features

Categories and Features

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