AI-driven fuzz testing improves test coverage by generating more intelligent and diverse test cases. This evolution makes fuzz testing more effective in detecting security and reliability flaws.

1. Why Fuzz Testing is Essential for
Security Assurance

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We’ve all experienced an app crash at some point, whether while
uploading a file, entering our login details, or performing any routine
user action. The experience is never pleasant.
Now, imagine you’ve released an app into the market, and the moment
users start interacting with it, they encounter an unexpected crash,
which could be caused by a variety of factors, including file uploads,
memory leaks, or Denial of Service (DoS) attacks.
You’re left with dissatisfied users, a long list of error logs, and a
tarnished brand image. This is your worst nightmare, right? But wait,
that doesn’t end here. Every time you add a new feature or update your
code, there’s a possible chance of failure.
So, what’s the solution? Fuzz testing. In this blog post, we’ll explore
everything you need to know about it: what fuzz testing is, the most
effective techniques to use, and the best practices to follow. Let’s get
started.
What Is Fuzz Testing?
Also known as fuzzing, it’s an automated software testing technique
where a computer program is deliberately exposed to invalid,
malformed, or random data to uncover hidden bugs and vulnerabilities
that otherwise wouldn’t have been revealed through traditional testing
methods.
A fuzzer is a tool that generates these random test inputs based on a
specific predefined set of values and injects the data into the program.

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The primary purpose of this type of test is to determine if the app
crashes or behaves abnormally when subjected to stress.
Fuzz testing helps identify security flaws in the source code, system
instability, and input handling errors. It also highlights why knowing
how to write test cases effectively is important—because even the
most detailed manual test cases can miss the kind of unexpected
errors fuzzing uncovers.
Origin of Fuzz Testing: The
Story Behind It
Fuzzing was first discovered by Professor Barton Miller at the
University of Wisconsin in 1988. He was working on a project to test
the reliability of UNIX command-line programs. To assess the UNIX
utilities, he fed a large number of random inputs into the system until
it crashed.
The failures revealed the weaknesses of the app. Miller’s team then
debugged the crashes to observe the reason behind them. Later, the
source code, testing techniques, and result data were made publicly
available, allowing other researchers to conduct similar tests.

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Fuzz Testing Types
1. Grey box fuzzing
This form of fuzzing falls somewhere in the middle, striking a balance
between black-box and white-box fuzzing. The data is generated
based on partial knowledge of the app. The fuzzer uses feedback to
create inputs. Complete access to the source code is not required.
Grey box fuzzing can be used for testing apps where code
instrumentation or runtime feedback is available, such as open-source
projects, compiled binaries, and APIs.
2. Black box fuzzing
This is the most commonly used fuzzing method by testers. Black box
fuzzing generates random data without any knowledge of the target
app’s internal structure. The advantage here is that you don’t need
access to the source code. But then, it might miss complex bugs.
Black box fuzzing is apt for testing closed-source third-party web
apps, protocols, and APIs. It checks the overall robustness of the app.
3. White box fuzzing
Here, the tester has complete knowledge of the source code and
internal structure of the app being tested. The input data is generated
based on its operation. The approach is more targeted and, hence,
more effective at finding intricate vulnerabilities.

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White box fuzzing is ideal for testing sensitive encryption algorithms
and memory management functions. The test is directly integrated
into the development phase, where you have access to the source
code.
Fuzz Testing Benefits
A fuzzing tool probes every corner of your app, unearthing code
anomalies. Here’s how:
1. Detects hidden bugs early
Edge cases and rare bugs can snowball into bigger issues
post-deployment if left untested. Fuzzing exposes your app to
unexpected and invalid inputs to detect hidden vulnerabilities, such as
memory leaks and buffer overflows, during the early development
cycle. This helps reduce both the time and cost of fixing these issues
at later stages.
2. Checks system robustness and security
Your app should be able to handle a wide array of inputs without
malfunctioning or leaking sensitive data. Fuzzers often simulate
real-world inputs, such as gibberish text, various file formats (PDF,
PNG, ZIP), configuration files (.json, .xml, .yaml), and protocol data
(HTTP, DNS, FTP), to test how the system reacts.

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3. Improved reliability and stability before deployment
After release, crashes can cost you users and money. Instead of
manually sorting through hundreds of crash reports, automated bug
triage quickly highlights the most serious issues and filters out
duplicate reports. This lets your team fix the most important problems
faster.
How to Perform a Fuzz Test
Conducting a fuzz test depends greatly on your app deliverables.
Here’s a simple strategy to get you started.
1. Define fuzz target
First things first, you need to identify the target areas you want to test.
Focus on every data entry point, such as user inputs, file parses, API
connections, encryptions basically, wherever attackers can potentially
exploit data.
Threat modeling helps you identify potential threat agents (such as
hackers, malware, and third-party APIs) that could cause harm to your
app. You can select fuzzing targets based on who your potential threat
agents are and what their likely entry points are.
For example, fintech apps are prone to identity theft, phishing, and
malware attacks. So, your fuzz target should be login & authentication
APIs, as well as multi-factor authentication. And password recovery
flows.

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2. Choose the right fuzzing tool
The fuzzing tool you select depends on the programming language
you’re working with, the test target, and the complexity or scale of your
project.
You can choose a ready-made tool, such as PeachFuzzer, OSS-Fuzz, or
LibFuzzer, or develop one on your own. Either way, it must meet
requirements unique to your app.
3. Generate test data
A fuzz testing output is only as good as the input test data you
generate. Whether you choose random generation, mutation-based
generation, or template-based generation, the idea is to create input
that helps you catch even the slightest deviation in the app’s behavior.
Your test data should ideally include malformed inputs such as typos,
accidental key presses, or long strings of text, as well as simulate
malicious payloads, large numbers, and empty fields.
4. Execute the fuzz test
Once you have the input data in place, it’s time to put it into action.
The fuzzer begins feeding that data into your app and closely tracking
it for abnormalities and failures. In this stage, you must observe for
any unexpected termination of the app, memory leaks, and abnormal
resource usage.

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5. Report anomalies and fix them
Fuzz tests generate a substantial amount of data. Therefore, it’s
essential to monitor every crash and verify whether it is legitimate.
Once you have executed the input, it’s time to analyze the crashes.
Your fuzz testing tools must have already recorded the input that
caused the crashes.
Next, reproduce the input data and verify its legitimacy. use debugging
tools like GDB, PyCharm, and LLDB to evaluate stack traces, memory
content, and variables responsible for causing the crash.
You can analyze the causes and develop viable methods to address
them. Fuzzing isn’t a one-time thing. You must refine and generate
new test cases that delve deeply to identify specific vulnerabilities.
Common Tools for Fuzzing
1. ZZUF
ZZUF is a transparent app input fuzz testing tool that works by
intercepting system calls and making random alterations in the input
data to test how well the app handles the tweaks. It’s well-suited for
fuzzing file formats.
2. LibFuzzer
LibFuzzer is built to perform coverage-guided fuzzing. It feeds fuzzed
inputs via a target function, tracks bugs or crashes, and generates

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mutations to explore new code paths. It’s highly compatible with
C/C++.
3. Peach Fuzzer
Peach Fuzzer is an innovative fuzzing tool that enables both
generation- and mutation-based fuzzing. It is primarily used to fuzz file
formats, APIs, and network protocols.
4. Google OSS-Fuzz
Google OSS-Fuzz is a free fuzz testing platform. It supports C/C++,
Python, Java, Rust, Go, and several other languages, and provides
comprehensive coverage through continuous testing. It’s ideal for
large-scale open-source projects.
5. Google ClusterFuzz
Google ClusterFuzz is a scalable fuzzing infrastructure designed
mainly to run continuous fuzz tests on Google products. The best part
is that it can run thousands of tests in parallel and supports multiple
fuzzers, such as AFL and libFuzzer. Google ClusterFuzz is the fuzzing
backend for OSS-Fuzz.

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Fuzz Testing Best Practices
1. Code instrumentation for comprehensive coverage
Code instrumentation helps you track the parts of code that are being
tested by feeding additional instructions into the source code or
binary. The fuzzer uses the feedback to identify areas that have not
been tested and modifies the input to test those areas.
The aim is to make the test coverage as comprehensive as possible
and effectively find edge cases without unnecessarily wasting time on
already tested paths.
2. Integrate fuzzing into CI/CD pipelines
Continuous fuzzing throughout your software development lifecycle
(SDLC) enables quicker feedback to developers, who can work on
fixing the bugs before they become a bigger problem.
Note that fuzzing can take a considerable amount of time, depending
on your input volume and target, so be mindful not to block the CI/CD
pipelines for too long. Setting fixed periods dedicated to fuzz tests
can help sort out the issue.
3. Combine other fuzzing techniques
Fuzz testing is just one part of the equation. It doesn’t make your app
foolproof. Combining other testing methods, such as integration/unit
testing, as well as manual code reviews, can reveal more significant

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faults in the system. This multi-faceted testing approach improves
your security posture and creates a more robust app.
4. Update fuzzing tools regularly
Testing tool models evolve rapidly. Updates come with enhanced
features and comprehensive coverage capabilities to safeguard your
app against malicious attacks. Check for upgrades or subscribe to
local repositories to stay current.
Explore our guide on test cases for login pages to see how proper
testing and automation improve security and reliability.
Fuzz Testing Is a Continuous
Process
Every change in the codebase requires you to run a fuzz test and
check whether the updates triggered any new vulnerabilities. TestGrid
is an end-to-end AI testing platform that can enhance your fuzz testing
strategy by integrating it into your continuous testing pipeline.
With its AI‑driven automation and support for real devices and
browsers, you can run fuzzing scripts alongside functional and UI
tests, covering every part of the app.
Source: For more details, readers may also refer to TestGrid.