What are some common mistakes in fuzz testing?
An example of an AI chatbot conversation about common fuzz testing mistakes.
What are some common mistakes in fuzz testing?
Some common mistakes in fuzz testing include not defining clear objectives, using inadequate input data, and failing to analyze results properly.
Can you elaborate on how to define clear objectives?
Defining clear objectives involves specifying what you want to achieve with fuzz testing, such as identifying security vulnerabilities or performance issues.
What types of input data should I use?
You should use a variety of input data types, including valid, invalid, and edge case inputs to thoroughly test the application's behavior.
How can I improve my analysis of the results?
Improving analysis can be done by using automated tools to track issues, categorizing findings, and prioritizing them based on severity.
Are there any tools you recommend for fuzz testing?
Yes, some popular fuzz testing tools include AFL (American Fuzzy Lop), LibFuzzer, and Peach Fuzzer.
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