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A guide to fuzz testing

Unlock the secrets of fuzz testing with our expert guide. Explore smart vs. dumb fuzzers, discover cutting-edge AI advancements, and master the tools shaping software testing. Your journey to becoming a fuzz testing pro starts here!

Written byMatt Valley
Published OnSun Feb 04 2024
Last UpdatedSun Feb 04 2024

What is fuzz testing?

Fuzz testing is a type of automated software testing; a method of discovering bugs in software by providing random input to the software under the test and monitoring any crashes and failed assertions. Fuzzer is a computer program that offers random inputs to the software. Fuzz testing can be applied to virtually any software program, including HTTP APIs.

Smart vs. dumb fuzzing

As mentioned earlier, a fuzzer provides random data to the software under the test. The input can be entirely random without matching the shape of the expected input or generated to match a valid input.

Dumb Fuzzer

Dumb fuzzers produce completely random input that does not necessarily match the shape of the expected input. Lack of built-in intelligence about the software it’s fuzzing makes this type of fuzzer a dumb one. Due to their simplicity, dumb fuzzers can produce results with little work. On the other hand, dumb fuzzers may be able to fuzz only certain areas of the software.

For example, the presence of the newsletter boolean field in the request payload of a Rest API endpoint for creating a new user triggers the logic for newsletter subscription. But, as you may have already guessed, a dumb fuzzer won’t be able to begin the execution of the newsletter subscription logic and identify potential bugs in that area of the code due to the wholly randomized input that does not match the valid input.

Pros & Cons of dumb fuzzing

Dumb fuzzing prosDumb fuzzing cons
Straightforward to set up, run, and maintainLimited code coverage due to the fully randomized input
Requires minimum amount of work for the initial setupSometimes, it tests a parser than your program

Smart Fuzzer

Smart fuzzers push the boundaries of fuzz testing by generating randomized data valid enough to pass program parser checks, get deep into the program logic, and potentially trigger edge cases and find bugs.

The more built-in intelligence you add into your smart fuzzer, the greater code coverage you will have.

Pros & Cons of smart fuzzing

Smart fuzzing prosSmart fuzzing cons
Greater code coverage in comparison with dumb fuzzersRequires more work to set up, run and maintain
Catches more bugs thanks to greater code coverage

Fuzzer types

Based on the way a fuzzer generates the randomized input data, we can divide fuzzers into mutation-based and generation-based fuzzers.

Mutation-based fuzzers

A mutation-based fuzzer takes valid inputs and generates a collection of inputs by changing (mutating) the valid inputs.

Generation-based fuzzers

A generation-based fuzzer analyses the provided valid input structure and generates entirely new data that matches the valid one from the structure perspective.

Generating random data

Mutation-based and generation-based fuzzers both generate random data, albeit taking different approaches. The experience has proven that including specific values can trigger edge cases and bring bugs to the surface. The table below goes through some values proven to bring bugs to the surface.

Empty stringsSometimes, empty string by-pass missing value checks and trigger bugs
Long stringsBugs as a result of truncation come to the surface as a result of passing long strings to programs
Strings with variant lengthShort, medium, and long strings can trigger bugs as well
0Similar to empty strings, value 0 can sometimes pass the missing value checks and trigger bugs
Negative numbersTriggers bugs related to assuming positive numbers but lacking validation for that
DecimalsTriggers bugs related to assuming integers but lacking validation for that
Special charactersBring up bugs related to embedding values in URL or saving in database
Max / Min numbersDoes the code cope well with a maximum allowed number? what about the minimum?

Code coverage in fuzz testing

Code coverage refers to the percentage of the executed code while running the test cases against the source code. Broadly speaking, the greater the coverage percentage, the better it is. Therefore, while doing fuzz testing, you should always keep an eye on the part of the code you fuzz. For example, a dumb fuzzer can potentially fuzz the parser code than your business logic. Moreover, a mutation-based or generation-based fuzzer will trigger the execution of the code in different branches of the source code.

The diagram below shows seven branches of code that a fuzzer can trigger via input. But, of course, not every fuzzer can start all branches.

Signup flow with 7 different branches

Why should you keep an eye on API Fuzzing?

When it comes to keeping our apps secure, we perform various kinds of testing methods such as static code analysis, dynamic code analysis, penetration, and more to protect them from the eyes of attackers. But usually, APIs are in the blind spot of the application security testing methods.

Unfortunately, in many software companies, security leaders think their APIs and products are safe because of performing regular penetration testing, not knowing the hackers find the software vulnerabilities through fuzzing. So, here is the fun fact, if you want not to be a victim of hackers, think like a hacker and find your software loopholes.

Consider the fuzzing practice in your penetration tactics and techniques for more accurate results about API vulnerabilities.

What are the taxonomies of API fuzzers?

Different companies have various approaches to finding vulnerabilities through fuzzing, but Microsoft’s taxonomies are generally more common and accepted.

Knowledge of Input

Some called them “smart fuzzers,” as we discussed previously in this article; they know the expected input format of the application. So, for example, they know that the input file should be in text format.

Knowledge of target application structure

There are also different categories for fuzzers I want you to hear: black-box, white-box, and gray-box fuzzers.

In the white-box approach, the tester or the test tool has all the required information of the input format and structure of the target, so there is no need to guess them in the opposite of the black-box testing approach.

So in the world of fuzzers, the white-box fuzzer who knows all about the target application, and the gray-box fuzzer who has partial knowledge of the target application, are the smartest fuzzers.

Method of generating new input

As discussed earlier in this article, fuzzers can randomly generate new input from scratch or change the pre-existed input to test the target with them.

Fuzz testing APIs using Testfully

Testfully’s embeddable value data generators and data templates enable our customers to easily create intelligent fuzzers based on the expected request payload shape while controlling the generated data to test different branches of the code. This feature is available under all plans (including free plan).

The below short demo shows how easy it is to add random data to your requests.

Top API fuzzing tools


Website: | Price: Free and Open source

A content discovery tool that uses many open API’s specifications as a searching source to find unlinked files or folders. Also, it integrates with apps like Burp to replay the test with different permutations of the original request.


Website: | Price: Free and Open source

It is the world’s first stateful API fuzzer, a Microsoft product that gets the open APIs specification and generates the tests automatically to find the vulnerability of an API.

Fuzz testing glossary

FuzzThe random input for fuzz testing
FuzzerA program or programming code that generates random input
Dumb fuzzerA fuzzer that does not know the expected input structure
Smart fuzzerA fuzzer that knows input structure
Mutation-based fuzzerA fuzzer that generates input by changing the provided valid input
Generation-based fuzzerA fuzzer that generates input from scratch by analyzing the provided valid input
Code CoverageThe percentage of the code that is executed by running the test cases
Source code branchA portion of the source code that will be executed under certain conditions

Latest Advancements in Fuzz Testing

Software testing constantly evolves, with fuzz testing leading the way in uncovering critical vulnerabilities. Recent advancements, particularly the integration of AI, have revolutionized fuzz testing practices.

Integration of AI in Fuzz Testing

Integration of Artificial Intelligence (AI), particularly Large Language Models (LLMs), has significantly improved fuzz testing. Google, a pioneer in this field, has integrated LLMs into its OSS-Fuzz project, aiming to push the boundaries of traditional fuzz testing. The integration has shown promising results, especially in improving code coverage - a critical factor in the effectiveness of fuzz testing. For instance, in the tinyxml2 project, integrating LLM-generated fuzz targets increased code coverage from 38% to 69%, all without human intervention. This substantial improvement underscores the potential of AI in automating and enhancing the fuzz testing process, making it more efficient and far-reaching​​.

Fuzzing Competitions and their Impact

The field of fuzz testing is becoming increasingly competitive, with platforms such as Google’s FuzzBench leading the charge. These platforms organize competitions where different fuzzing tools are pitted against each other and are evaluated based on their code coverage and bug discovery capabilities. These competitive analyses are not merely academic exercises; they ensure fuzz testing methodologies and tools are robust and efficient and handle modern software complexities.

Expanding the Scope of Fuzz Testing

Fuzzing in Memory-Safe Languages

The expansion of fuzz testing into memory-safe languages, such as Rust, is a significant shift from the traditional focus on languages prone to memory safety issues. Google’s commitment to fuzzing in Rust, despite it being a memory-safe language, has led to the discovery and remediation of numerous non-security issues, thereby enhancing the stability of systems. The following points highlight the importance of fuzz testing in memory-safe languages:

  • Even in memory-safe languages, fuzz testing helps uncover non-security-related issues.
  • Fuzz testing contributes to the overall system stability by identifying and addressing these issues.
  • The expansion into memory-safe languages demonstrates the versatility and essential role of fuzz testing in modern software development​​.


Fuzz testing is an essential part of software testing, providing a reliable way of finding hidden bugs and vulnerabilities. This method involves feeding software with various random inputs to challenge applications unexpectedly, exposing weaknesses that may have remained undiscovered otherwise. Fuzz testing is not merely about creating chaos in a system; it is a complex process that requires intelligent strategies and tools to ensure thorough coverage and effectiveness.

From the straightforward yet limited dumb fuzzers to the more complex and insightful smart fuzzers, the methodology caters to different testing needs and resource availabilities. Moreover, the evolution of fuzz testing, with the integration of AI and the expansion into memory-safe languages, marks a significant leap forward, enhancing the capabilities of this testing method to meet the demands of modern, complex software systems.

In conclusion, understanding and leveraging fuzz testing is crucial whether you’re a developer, a security professional, or simply someone passionate about software quality. It’s not just about finding bugs; it’s about fortifying your software against the unexpected, ensuring reliability, security, and trust in the digital solutions that power our lives.

Frequently Asked Questions

We got an answer for your questions

  • What exactly is fuzz testing?

    Fuzz testing is a software testing technique where random data (the fuzz) is input into a program to find issues like crashes or failures. It's like testing the program's endurance by throwing unexpected scenarios.

  • What's the difference between dumb and smart fuzzers?

    Dumb fuzzers don't know what the program expects and send random data. Smart fuzzers, however, are more informed and send data that's more likely to navigate through the program's logic, uncovering more complex issues.

  • Why is fuzz testing necessary?

    Fuzz testing helps find bugs that other testing methods might miss, especially those that could lead to crashes or security breaches. It's like a stress test for your program to ensure it can handle unexpected inputs gracefully.

  • Can fuzz testing be automated?

    Yes, fuzz testing is typically automated. You set up a fuzzer, which runs through loads of random inputs on its own, recording any issues it finds for you to fix later.

  • What advancements have been made in fuzz testing?

    AI has been a game-changer in fuzz testing, making it more intelligent and efficient. For example, AI can help improve how well a fuzzer understands a program, leading to better test coverage and more bugs found.

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