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What is Sulley?

Sulley serves as a robust fuzz testing framework and engine that integrates a variety of extensible components. In my opinion, it exceeds the capabilities of most prior fuzzing tools, whether they are commercially available or open-source. The framework is intended to simplify not just the representation of data, but also how it is transmitted and instrumented. As a fully automated fuzzing solution crafted entirely in Python, Sulley functions independently of human oversight. Alongside its remarkable data generation abilities, Sulley boasts numerous essential features typical of a modern fuzzer. It diligently monitors network activity while maintaining comprehensive logs for in-depth analysis. Moreover, Sulley is designed to instrument and assess the stability of the target system, with the ability to restore it to a stable condition using various methods when necessary. It proficiently identifies, tracks, and categorizes any issues that occur during testing. Furthermore, Sulley can execute fuzzing tasks concurrently, significantly increasing the speed of the testing process. It also has the capability to autonomously discover unique sequences of test cases that trigger faults, which enhances the overall efficiency of the testing procedure. Additionally, Sulley’s extensive feature set makes it an invaluable asset for security testing and vulnerability assessment. Its continual evolution ensures that it remains at the forefront of fuzz testing technology.

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
Python

Integrations Supported

Atheris
C
C++
ClusterFuzz
Fuzzbuzz
Google ClusterFuzz
Jazzer
Python

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

OpenRCE

Company Website

github.com/OpenRCE/sulley

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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