3 Best Practices For Fuzz Testing

Any software development company worth its salt knows the value of secure software. Not only it is essential to protect their product, but it’s also important to make sure that users are safe from data breaches, and that their privacy is protected.

Data breaches that occurred in 2020, according to a report by Risk Based Security, resulted in 37 billion records that were compromised, which was a 141% increase from the previous year.

Statistics like these are among the reasons why security is crucial. That’s why a company that takes security seriously is regarded by consumers as trustworthy. Clients will always prefer to do business with software development companies that can take better care of their data.

Software developers are keenly aware of this; consequently, security is now an integral part of SDLC or software development lifecycle. They’ve put in place software testing techniques to ensure that their software stays secure. And one of the most popular testing techniques that they use is called fuzz testing.

An Overview On Fuzz Testing

As incidents of cyberattacks continue to increase and cybercriminals find new ways to find and exploit software vulnerabilities, techniques like fuzz testing are becoming more crucial than ever.

Today, software developers have incorporated security, including fuzz testing, into all stages of software development. They’re also using a methodology called the DevSecOps program that automatically incorporates security into every phase of SDLC.

Fuzz testing is a testing technique that looks for exploitable software vulnerabilities by feeding a computer program random, invalid, or unexpected data. The unstructured data would make a vulnerable system crash; programmers would then use a software tool referred to as a ‘fuzzer’ that could pinpoint possible vulnerabilities.

The random, unstructured data are called ‘fuzz’ by the technique’s inventor, computer science professor Barton Miller of the University of Wisconsin-Madison. According to Professor Miller, the ‘electrical noise’ produced by a thunderstorm one stormy night in 1988 was causing distortion or interference to the inputs he was creating for a computer program.

The unexpected data, which wasn’t what the program expected from any user, resulted in errors in the program he was using. What was surprising, added Professor Miller, was that the unexpected data input even crashed programs that he thought were stable. A robust, stable program should have discarded the error and would just ask for valid input.

He and his students, over several years, thereafter conducted research and ‘fuzz tested’ Mac, Linux, and Windows apps. Their fuzz testing caused a few hangs and crashes in these systems, exposing a host of bugs and vulnerabilities in the process. Thus, fuzz testing as a technique to check for weaknesses in a computer program was born.

As useful as fuzz testing is, it’s still imperative that you follow the technique’s best practices to fully realize its benefits.

Best Practices For Fuzz Testing

Fuzz testing is best for uncovering weaknesses that can be exploited by Denial-of-Service Attacks (DoS), using SQL injection, cross-site scripting, and buffer overflow. Cybercriminals exploit these vulnerabilities to incapacitate security, usually to pilfer data or bring down an entire system.

It used to be that fuzz testing is the purview of security experts. But now, the technology has sufficiently advanced that even non-experts can learn to conduct this test in no time. To get this testing technology to maximize its usefulness, here are a few ways to make sure that fuzz testing is done effectively:
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1. By Strictly Following The Phases Of Fuzz Testing

There are six phases of fuzz testing. These are the following:

• Identify Target System

Mark the software application or system for testing, which will be designated as the target system by the testing team.

• Determine Inputs

The random data inputs that will be converted as ‘fuzz’ for the test are created after the target system has been prepared. The interface for the input data’s insertion is also created.

• Create Fuzzed Data

After compiling random inputs, that is, the invalid and unstructured data, they’d then be converted into fuzzed data—the data, or the ‘fuzzy logic,’ that will be inserted into the system.

• Implement The Fuzz Test

The phase where the actual fuzz test happens, using the fuzzed data.

• Observe System Behavior

After the test implementation, the behavior of the software application or the system is monitored for possible security weaknesses and vulnerabilities, including memory leaks, crashes, lags, and others.

• Record Defects

Lastly, the defects are logged, identified, and addressed. These defects are handled by the developers before a product is released.

2. Understand The Different Approaches For Fuzz Testing

For best results, it’s essential to learn about these two algorithm approaches or strategies for fuzzing that you can use:

• Generation Fuzz Testing – This approach, which is also called ‘intelligent’ fuzz testing, differs from ‘dumb’ fuzzing in that your testing engine has an understanding of the program’s interface and its valid inputs. The fuzz inputs are created separately from any previously generated input. This means that fuzzers are generated or created from scratch without referencing previous fuzz inputs. This approach is more tedious and takes a lot of work. However, it’s deemed to be a more thorough process.
• Mutation Fuzz Testing – Also called ‘dumb’ testing, this approach randomly ‘mutates’ or alters valid inputs to produce the needed fuzz inputs. This can be done even without knowing about the program or protocol.

3. Merge Two Types Of Fuzz Testing

Merging these two types of fuzz testing can produce more efficient and thorough results:

• Coverage-Guided – This type of fuzzing utilizes related data based on a source code to make inputs for the tests and comparison of the resulting system hangs or crashes. This type of fuzzing also shortens cycle time, or the time needed to finish a product; in this case, your software or application. Moreover, coverage-guided fuzzing can also give you an update on the location of the vulnerable or exploitable areas.

And because you can focus this test only on the application’s regions where there might be vulnerabilities, you can do this test on an incomplete product. What this means is that you can incorporate fuzzing much earlier in the SDLC. The earlier you can find vulnerabilities in your program or app, the quicker developers can fix them. This would result in a shorter cycle time.

• Behavioral Fuzzing – This fuzzing type (also called ‘traditional’ fuzz testing) utilizes inputs in finding out the efficacy of its Implementation Under Test (IUT). It lets fuzz testers create mutations that are almost, but not quite, valid inputs. This is useful for fine-tuning a test, resulting in better quality outcomes by provoking more weaknesses to be exposed and locating them in the application.

Combining these two would mean the tester would have a better understanding of the reasons an app or software has bugs, or the reasons for the crashes and hangs.



With this understanding, the tester can create more cases for testing other areas of software or an app’s code. It would also be easier for the testers to find out the exact features of the product that needs to be resolved.
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Final Word

Incorporating security in SLDC, like what DevSecOps does, is a crucial advancement in software development. Cybercrimes have increased in recent years and security is more important than ever.

With fuzz testing, vulnerabilities are exposed, and developers would be taking a more proactive stance in dealing with possible weaknesses in their products. Vulnerabilities would be exposed and weaknesses patched up as early as possible.