API Fuzz Testing: Why should you use it? in 2025

Around 95% of APIs suffer from security issues, and Only 11% of businesses have a thorough API security plan incorporating API testing. ¹ Fuzz testing can solve security weaknesses and more since: 

We explore fuzz testing, its importance, and the different types of testing used by 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: 

• DDoS attacking.
• SQL injecting. 

Fuzz testing is well equipped for identifying zero-day vulnerabilities which are vulnerabilities identified by attackers before vendors identify them. However, fuzz testing only is not enough to 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 via practices such as:

• Penetration testing
• Static Code Analysis

Pentesting tools, can be used to carry out these practices.

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

Source: INFOSEC ²

Benefits:

• Mutational fuzzing is relatively easy to implement. Once a valid test case exists, simply making random changes can generate new test cases without deep knowledge of the software’s structure.
• Mutational fuzzing allows the reuse of existing test cases, reducing the burden on testers to create new test inputs from scratch.
• Good coverage of input space, meaning it can effectively explore a wide variety of input possibilities by systematically mutating valid inputs.

Drawbacks:

• It may fail by protocols that use checksums and other rigorous checks.
• It may struggle with highly structured inputs like XML or JSON, where a small mutation can easily make the input invalid, resulting in reduced effectiveness and wasted resources.
• Because mutational fuzzing often generates a vast number of test cases (many of which may be invalid), it can be resource-intensive in terms of both computation and time. 

Generational fuzzing 

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

Benefits:

• For inputs that follow strict formatting rules (e.g., XML, JSON, network protocols), generational fuzzing can generate valid inputs that adhere to these formats. This increases the likelihood of reaching deep into the application’s logic and finding bugs.
• With an understanding of the input structure, generational fuzzers can create test cases specifically designed to trigger specific parts of the code, making them more effective for testing logic-based vulnerabilities.

Drawbacks:

• Generational fuzzers require detailed knowledge of the input format or protocol being tested. Developing the model or specification that guides the generation of test cases can be time-consuming and require deep expertise.
• Since generational fuzzing focuses on well-formed, valid inputs, it may be less effective at finding memory corruption bugs, which often arise from malformed or unexpected inputs. Mutational fuzzing can be better for finding such low-level bugs.

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. 

Benefits:

• Small chance of false positives
• Easy to implement as testers do not need to know implementation details 
• Since black box testing focuses on inputs and outputs, it can be applied to various types of testing, including functional, usability, performance, security, and integration tests.
• Black box testing allows testers to focus on ensuring the software works as intended, without needing to study complex internal components.

Drawbacks:

• Since the tester has no visibility into the internal code, black box testing might miss certain types of errors, particularly those related to the internal logic or structure of the software.
• Creating thorough test cases can be challenging, especially when the software has complex input/output combinations. Testers

Grey box

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

Benefits:

• With partial knowledge of the system, testers can better design test cases that cover a wider range of inputs and outputs.
• It is especially effective for security testing, where knowledge of the internal structure (such as session management, encryption methods, or input validation techniques) can help testers craft more targeted attacks to identify vulnerabilities.
• Gray box testing can detect defects at an early stage, especially in cases where the interaction between components, data handling, or input validation is critical. 

Drawbacks:

• Having incomplete knowledge of the system’s internals can lead to less effective test cases for certain areas, especially if the system’s architecture is complex or poorly documented.
• Gray box testing is less suited for finding low-level issues like memory leaks, race conditions, or performance bottlenecks, which typically require white box testing methods that involve analyzing the code structure and execution paths in detail.

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. 

Benefits:

• Automatable 
• Provides higher code coverage
• For complex applications with intricate logic, white box testing is effective because it ensures all logical paths and edge cases are verified.

Drawbacks:

• Expensive as it requires skilled testers with programming knowledge 
• High sensitivity to code changes
• As the application evolves, changes in the code might require revising or rewriting tests, leading to higher maintenance costs.

Figure 2. Black box vs grey box vs white box 

Source: Coders Kitchen ³

External Links

• 1. Salt Security “Companies are Struggling Against a 681% Increase in API Attacks, the Latest “State of API Security” Report Shows”
• 2. INFOSEC “Fuzzing: Mutation vs. generation”
• 3. Coders Kitchen “Fuzzing techniques – The Generator Menace”