Fuzzing is an automated black-box testing method used to uncover bugs and implementation flaws in software. It’s not an application simply for exploiting a specific application but instead, you can use it to acquire knowledge and find out potential application crashes due to the bad practices of coding. Fuzzing is most effective for identifying vulnerabilities that can be exploited by buffer overflow, denial of service (DoS) attacks, cross-site scripting (XSS), and SQL injection.
Brief History of Fuzzing.
Fuzz testing was developed at the University of Wisconsin Madison in 1989 by Professor Barton Miller and students. Miller noticed considerable signal interference that led to a system crash while he was logging into a UNIX system over a dial-up network during a storm. In response, he instructed his students to mimic his encounter by flooding UNIX systems with noise using a fuzz generator to determine whether they would also crash. You can find their continued work at It was basically focused on command-line and UI fuzzing and it shows that modern systems are vulnerable to even simple fuzzing.
Two Types of Fuzzing
There are two primary types of fuzzing: coverage-guided and behavioral.
Coverage Guided Fuzzing:
Coverage-guided fuzzing focuses on probing the source code with random input while the application is running to reveal any bugs. The primary objective is to get the application to crash, as this indicates a possible problem. The coverage-guided fuzz testing process continually generates new tests, and the data obtained enables a tester to reproduce the crash, making it easier to identify potentially problematic code.
Behavioral fuzzing functions differently. It uses specifications to demonstrate how an application should work and uses random inputs to evaluate how the application actually functions. Typically, any variance between what is expected and what is observed represents potential bugs or security risks.
Advantages of fuzzing over other security testing methods
Fuzzing is a testing methodology that differs from traditional software testing approaches such as SAST, DAST, or IAST. Rather than systematically analyzing the code or the environment in which the code runs, fuzzing involves repeatedly “pinging” the code with random or unexpected inputs to identify faults or vulnerabilities that may not be immediately apparent. The goal of fuzz testing is to intentionally trigger errors or crashes in the code, in order to identify and resolve weaknesses that might be exploitable by attackers.
Benefits of Fuzzing
Fuzzing offers several benefits for improving software quality and security: Here are some of them.
Finding bugs: Fuzzing can give a comprehensive view of the quality of the targeted system and software. It can help identify hard-to-find bugs, including those that are not detectable by traditional testing methods. By generating random inputs and testing the application with unexpected and invalid data, fuzzing can uncover errors that might otherwise go unnoticed.
Improving security: Fuzzing can identify security vulnerabilities that could be exploited by attackers. By detecting buffer overflows, DoS, XSS, SQL injection, and other vulnerabilities, fuzzing can help developers identify and address these security issues before they can be exploited.
Cost-effective testing: Fuzzing is an automated testing technique that can be performed with minimal human intervention. This makes it a cost-effective way to find bugs and security issues that might be difficult or time-consuming to detect using manual testing methods.
Improved testing coverage: Fuzzing can provide better testing coverage than traditional testing methods, which often rely on predetermined test cases. By generating a wide range of inputs, fuzzing can help test the application under different conditions and identify issues that might not be found using traditional testing methods.
Identifying performance issues: Fuzzing can help identify performance issues that could impact the usability and reliability of an application. By testing the application under different conditions, fuzzing can identify areas where the application may be slow or unresponsive, allowing developers to optimize its performance.
1. Exposure of Sensitive Information in Microsoft Windows
Michal Zalewski, the director of Information Security Engineering at Google, reported a vulnerability where memory initialization for TIFF images was not correctly performed in Windows Servers and Windows 7/8. This flaw enabled remote attackers to obtain sensitive information from process memory.
2.Out-Of-Bounds Read in Intrusion Detection System Suricata
Sirko Höer, an IT Security Consultant at Code Intelligence GmbH, discovered a bug in Suricata where a function attempted to access an unallocated memory area while sending multiple IPv4 packets with invalid IPv4Options. This led to the software attempting to read data beyond the intended buffer, which could result in a crash or unauthorized access to sensitive information in other memory locations.
3.Memory Corruption in Adobe Reader
This issue was exposed by Trend Micro’s zero-day initiative. Memory corruption is a situation where an application performs operations on a memory buffer, but it is able to read from or write to a memory location that is beyond the intended boundary of the buffer. This can lead to unexpected behavior, crashes, or security vulnerabilities.
Challenges And Limitations of Fuzzing
Practitioners who implement fuzz testing may face two main challenges: setup and data analysis. Setting up fuzz testing can be difficult and complex, as it requires the creation of testing “harnesses,” which can be even more challenging to develop if the fuzz testing is not integrated into an existing toolchain. Additionally, fuzz testing can produce a large amount of data, including potential false positives, so it is essential for testing teams to be prepared to handle the high volume of information.
Another big challenge is that it is less easy to document and the negative attitudes toward the “vague” nature of fuzz testing that still persist in the QA community.
How Does Fuzzing Workflow Work?
Fuzzing is an old mechanism that was developed by the University of Wisconsin in 1989 to detect potential implementation weaknesses. And to do this a specific fuzzer is used where semi-random data is injected to any program to detect bugs or potential crashes. There are several steps involved in the process such as:
- Identify target systems
- Identify inputs
- generate fuzzed data
- execute fuzzed data
- monitor system behavior
- Log defects
First of all, the target system is identified to select the specific fuzzer, the target input and right character to generate the final payload to test.
Once the target input is identified, the payload is generated. Data of various sorts, including strings, digits, characters, and combinations of them, may be included in various input sizes. The payloads are then executed by launching the fuzzer under the appropriate circumstances.
Another important part of the process is monitoring the system behaviors and finding the log defects. Here, using an offline approach, we can assess the test’s outcomes. During this stage you can find the potential flaw, bug or crashes.
Finally, the results can be used to generate a particularly designed payload.
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