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Description
Fuzzing serves as an effective method for identifying software bugs. Essentially, it involves generating numerous randomly crafted inputs for the software to process in order to observe the outcomes. When a program crashes, it usually indicates that there is a problem. Despite being a widely recognized approach, it is often surprisingly straightforward to uncover bugs, including those with potential security risks, in commonly used software. Memory access errors, especially prevalent in programs developed in C/C++, tend to be the most frequently identified issues during fuzzing. While the specifics may vary, the underlying problem is typically that the software accesses incorrect memory locations. Modern Linux or BSD systems come equipped with a variety of fundamental tools designed for file display and parsing; however, most of these tools are ill-equipped to handle untrusted inputs in their present forms. Conversely, we now possess advanced tools that empower developers to detect and investigate these vulnerabilities more effectively. These innovations not only enhance security but also contribute to the overall stability of software systems.
Description
Fuzz testing, commonly referred to as fuzzing, is a technique used in software testing that aims to discover implementation errors by injecting malformed or semi-malformed data in an automated way. For example, consider a scenario involving an integer variable within a program that captures a user's selection among three questions; the user's choice can be represented by the integers 0, 1, or 2, resulting in three distinct cases. Since integers are typically stored as fixed-size variables, a failure to implement the default switch case securely could lead to program crashes and various traditional security vulnerabilities. Fuzzing serves as an automated method for uncovering software implementation issues, enabling the identification of bugs when they occur. A fuzzer is a specialized tool designed to automatically inject semi-random data into the program stack, aiding in the detection of anomalies. The process of generating this data involves the use of generators, while the identification of vulnerabilities often depends on debugging tools that can analyze the program's behavior under the influence of the injected data. These generators typically utilize a mixture of established static fuzzing vectors to enhance the testing process, ultimately contributing to more robust software development practices.
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Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
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Business Hours
Live Rep (24/7)
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Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Fuzzing Project
Website
fuzzing-project.org
Vendor Details
Company Name
OWASP
Country
United States
Website
owasp.org/www-community/Fuzzing