The concept of fuzz testing isn’t new, it’s been there since long time. Tools like DirBuster, ffuf, and others have existed on GitHub for years, often relying on large wordlists or runtime string combinations to brute-force URL paths, input fields, or APIs. These are pretty common attacks for pen testers and they have been using this since ages.

These tools were especially useful for discovering forgotten admin panels, misconfigured endpoints, or error messages leaking sensitive data. Pen testers leveraged fuzz testing to find smaller issues and later they combine the smaller issues to discover bigger exploit. This is a common choice of attack for QAs participating in bug bounties.

But enterprise QAs barely touched these tools.

Because fuzzing never felt relevant to their day-to-day functional testing. Bombarding an app with millions of payloads for edge-case bugs felt inefficient or out of scope. Additionally, Enterprise QA lacks the exposure of security testing, so they lack the knowledge of creating a bigger issues out of edge cases discovered through fuzzing. On top of that, many companies outsource their pen testing to vendors. So fuzzing was never a preferred choice of enterprise companies for their in-house testing.

But now, Suddenly, I see fuzz testing picking up the pace again. Thats very much evident from its inclusion in the latest edition of Tech Radar published by ThoughtWorks. Now, enterprise companies are interested on investing time on effort to conduct fuzz testing within the company itself.

So, what’s changed in 2025 which made this technique famous again?

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The comeback of fuzz testing

In 2025, AI’s ease of integration made it popular again. Many enterprise teams, under immense pressure to “do something with AI,” saw fuzz testing as a low-effort, high-visibility checkbox. Thats the primary reason for it picking up the page again. But, it is still getting generalised which is not the right way of adopting this solution for in-house testing. There are certain unanswered questions for them prior to picking up the fuzz testing.

• Where does fuzz testing actually belong in a modern SDLC?
• Where’s the right entry point in a CI/CD pipeline? Is it part of PR validation? Nightly builds? Security test stage?
• How frequently should it run?
• Who are your end users? Would they ever trigger edge cases that a fuzzer mimics?
  If not, are we solving a real risk?
• Is the system mature enough to accept noisy inputs without flooding logs or alert systems?

If they could answer all of these. They are in a good situation to take a thoughtful decision.

But fuzzing just because they have a tool that does it? That’s not a strategy, that’s purely a checkbox compliance thing, which does not make any sense. Especially, when there is cost involved in using AI for this purpose.

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