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To protect your work from these harmful errors, having system security software is vital. Fuzz tests are the highest level software obtainable for identifying malicious bugs.

In this article you have everything you need to know about Fuzz Testin, what it is, how to do it, its advantages, and fuzzing tools.


What is Fuzz Testing Security?

Fuzz testing is an automatic software testing system that strives to detect implementation errors by strangely filling unexpected empty data and figures into a computer application to detect coding flaws and protection loopholes.

This method is used both by hackers investigating susceptibility to exploitation and by defenders seeking to detect and fix them.

Fuzz testing, or fuzzing, is a dynamic application security testing technique for negative testing. Fuzzing aims to detect known, unknown, and zero-day vulnerabilities.

A fuzz tool can be used to create a test case and send random or malformed input to the fuzz targets.

Their goal is to trigger bad behaviors such as crashes, infinite loops, and/or memory leaks.

These anomalous behaviors are often a sign of an underlying vulnerability.

Fuzz tests should be part of every SDLC. Examine the runtime behavior of the code. It provides more code coverage than SAST or SCA.


Why & How to Do a Fuzzing Test?

It is important to do Fuzz Testing because:

  • Fuzz tests help identify multiple flaws or serious security flaws.
  • Check the susceptibility of the software.
  • Delivers efficient results when used with Black Box Testing, Beta Testing, and other cost-effective debugging techniques and test methods.


A review of software security investments reveals that most of the spending goes to application testing solutions such as static analysis, software composition analysis, and scanners. However, these conventional testing approaches test known or common attack patterns and only target CVE or CWE.

But what about unknown vulnerabilities? Malicious hackers often exploit.

Fuzzing is a proven tool that maximizes defect detection with the least amount of time and resources.

As a result, it not only buys organizations time and money but also frees up scarce technical resources from mundane, manual tasks and allows them to focus on strategic initiatives that require real expertise.

For this reason, the world’s largest and most respected organizations are implementing fuzzing as part of their quality control and cybersecurity operations.

Now… The steps to perform this test are as follows:


  1. Recognize the system you are targeting.
  2. Identify all possible inputs.
  3. Then extract the fuzzy data.
  4. Run the test using fuzzy data.
  5. Then observe the behavior of the system.
  6. Record all resulting defects.



8 Best Fuzz Testing Software Tools

Below are some of the top open-source fuzz testing tools.

  • Radames – It is a general-purpose fuzzer for robustness testing. It is used to test how well an application supports malformed and potentially malicious input. You can read sample files of valid data inputs and generate different interesting outputs using them. Radames has already found various bugs in major programs and applications. It is easy to set up, it can be programmed and configured.Radames is more of a “black box” Fuzzer in that it does not require information about the program or the format of the data. However, you can optionally pair it with coverage analysis for testing and improving the quality of your sample set. Testers can start with simple tests and gradually refine the technique as needed.


  • Honggfuzz – It is a multithreaded fuzzer. This removes the requirement to run multiple copies of the fuzzer, as Honggfuzz can potentially use all of your available computing power with a single instance running. The file corpus is automatically shared and enhanced among all fuzzy processes.It’s blazing fast and has a strong track record of discovering security bugs. Honggfuzz discovered the only critical vulnerability (to date) in OpenSSL.


  • American Fuzzy Lop (AFL) – It is a free brute force fuzzer coupled with a solid but simple instrumentation-guided genetic algorithm. AFL uses a modified form of edge coverage to seamlessly capture subtle, local-scale changes in program control flow.AFL is thoroughly tested to deliver far superior out-of-the-box performance compared to blind fuzzing or coverage-only tools.



  • LibFuzzer – This is an evolutionary, coverage-driven, in-process fuzzing engine. It is linked with the library under test and provides fuzzy input to the library using a specific fuzzy entry point. The fuzzer can then track which code areas are hit and create mutations in the input data corpus to maximize code coverage.



  • Sulley – It is a Fuzzing engine and Fuzz testing framework consisting of several extensible components. Sulley is known to have surpassed the capabilities of most previously published fuzzing technologies, even in the public and commercial domains.

    The goal of the Sulley framework is to simplify data representation, data transmission, and instrumentation.


  • OSS-Fuzz – It was primarily designed to work with open-source software to make it more secure and stable. OSS-Fuzz supports open-source programs written in C, C++, Go, Rust, Python, and Java/JCM.

    It can also work with other languages supported by LLVM.


  • APIFuzzer – No coding required. Read your API description and you can move on to fuzzy fields to test if your application can support fuzzy parameters. APIFuzzer supports fuzzy stripping of the request body, route parameter, query string, and request header.


  • Jazzer – Developed by Code Intelligence, it is a coverage-driven in-process fuzzer for the JVM platform. It is based on libFuzzer and extends many of its instrumentation-driven mutation functionality to the JVM.The JVM bytecode is executed inside the fuzzer process, ensuring fast execution speed and seamless fuzzing of native libraries. Jazzer supports Linux x64 and is experimentally compatible with macOS 10.15.

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