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API Fuzz Testing in 2024: Importance & Different Types

Around 95% of APIs suffer from security issues, and Only 11% of businesses have a thorough API security plan incorporating API testing. Fuzz testing can solve security weaknesses and more since:: 

  • It identifies bugs and exploits that can be used by hackers, as fuzzing is one of the primary tactics that hackers use for exploiting.
  • It provides an overview of the quality of the API.
  • It reduces the time for testing as fuzz testing is an automated test.

In this article, we will explore fuzz testing, its importance, and different types of it for QA specialists. 

What is fuzz testing?

API fuzz testing is an automated testing method where random, invalid, distorted, or unexpected input is given to the API to see if any crashes or bugs emerge. Fuzz testing aims to identify unknown bugs and defects. 

Importance of fuzz testing

Hackers frequently employ fuzzing because it enables them to identify software flaws without having access to the source code. Vulnerabilities discovered by hackers can be used to exploit the API by: 

Fuzz testing is well equipped for identifying zero-day vulnerabilities which are vulnerabilities identified by attackers before vendors identify them. 

However, Fuzz testing does not provide a complete picture of the security status of the API. Further API security testing should be done to ensure the highest security for the API. Testers should do the following:


PULSE is an automated AI-driven API testing tool created by Testifi. PULSE can reduce the cost of testing by 50%. Market-leading companies such as BMW and Amazon use Testifi’s services. 

Fuzz testing can be categorized in several ways: 

  1. Level of input awareness ( Dumb vs. Smart fuzzing)
  2. Level of program structure awareness  ( Black vs. White fuzzing)
  3. How the inputs are generated ( Mutational vs. generational )

Dumb vs. smart fuzzing

Dumb fuzzing

Dumb fuzzing involved producing fully random data. The random data might not match the required structure and shape of the expected input. Dumb fuzzing will answer:

  • What inputs were provided to the program?
  • Did the inputs cause a crash?

Benefits: Dumb fuzzing requires minimum effort for setting up. Additionally, it is easy to execute and maintain.

Drawbacks: Dumb fuzzing provides limited coverage due to the full randomness of data. Additionally, many of the inputs will be rejected due to the mismatch between the data format and the required format for the field. 

Smart fuzzing

Smart fuzzing involves producing random data that match the required inputs of the API. 

Benefits: Provide greater code coverage than dumb fuzzing, which results in higher bug detection.

Drawbacks: Requires more effort for setting up, executing, and maintaining. 

Mutational vs generational fuzzing

Mutational fuzzing 

In mutational fuzzing, changes are made to inputs that have been accepted previously. This enables the generation of inputs without knowledge of the approved format that is likely to be accepted. For example, Bit flipping is a technique that is used in mutational fuzzing.

Figure 1. Mutational fuzzing



  • Easy to set and automate


  • It may fail by protocols that use checksums and other rigorous checks.

Generational fuzzing 

Generational fuzzing creates fully new random data based on the analysis of the provided valid input structure and format.


  • Can handle complex dependencies 


  • Hard to write and set

Black box vs. grey box vs. white box fuzzing 

Black box 

Black box fuzz testing is used when testers cannot access the source code (see Figure 2). This is also the method used by hackers. 


  • Small chance of false positives
  • Easy to implement as testers do not need to know implementation details 


  • Low  code coverage
  • Challenging to design test cases

Grey box

Grey box fuzz testing is used when only partial information is available and the full source code is unavailable. 

Benefit: strong programming abilities are not necessary for testers

Drawback: Challenging to design most test cases

White box

White box fuzz testing utilizes the information related to the program that is being tested to create inputs that have a higher likelihood of being accepted to find vulnerabilities. White box testing is more effective than black box testing, which is why software developers with access to the source code tend to use this method. 


  • Automatable 
  • Provide higher code coverage


  • Expensive as it requires skilled testers with programming knowledge 
  • High sensitivity to code changes

Figure 2. Black box vs grey box vs white box 

Source: Coders Kitchen

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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