Скачать или смотреть Mastering Input Constraints for Fuzzing in Go

Learn how to effectively set constraints on input data during fuzz testing in Go. This guide covers practical solutions, examples, and best practices for achieving accurate results in your fuzz testing.
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This video is based on the question https://stackoverflow.com/q/71698351/ asked by the user 'Liam Kelly' ( https://stackoverflow.com/u/1987437/ ) and on the answer https://stackoverflow.com/a/71754787/ provided by the user 'colm.anseo' ( https://stackoverflow.com/u/1218512/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Mastering Input Constraints for Fuzzing in Go: A Complete Guide

Fuzzing is a powerful technique used in software testing to uncover vulnerabilities by automatically generating random inputs. However, it can be tricky to apply when your data structures have specific constraints. In this post, we're going to address a common problem related to fuzzing in Go: how to set constraints on input for fuzzing so that you can test your code effectively without encountering unexpected edge cases.

The Problem Defined

Suppose we have a structure called Hdr defined in Go as follows:

[[See Video to Reveal this Text or Code Snippet]]

This structure represents a header with specific fields, some of which have constraints on their values. For example:

Priotity: This field should be limited to values between 0 and 15 since it only uses 4 bits when marshalled to a binary format.

Opcode: Similarly, this field needs to stay within a valid range to avoid unintended truncation or parsing errors in the binary representation.

The main challenge many developers face - including the original questioner - is how to instruct Go Fuzz to respect these constraints when generating random inputs.

Understanding Fuzz Testing

Before diving into solutions, let's briefly review fuzz testing's purpose. Fuzz testing is designed to expose edge cases that could lead to unexpected behavior or vulnerabilities. In cases where we know the exact structure and constraints of our input, regular tests (e.g., testing.T) might sometimes serve as a better fit.

Solution Strategy

1. Coerce Input Values

One approach to setting constraints within fuzz tests is to coerce values that fall outside acceptable limits. The following coerce function demonstrates this:

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In this function, we adjust the values of fields to ensure they meet the specified constraints. For instance:

The priority is masked to keep it within the acceptable range.

The opcode is constrained to a maximum value of 19.

2. Implementing Fuzz Testing

Once you have a coercion function, you can integrate it into your fuzz testing like so:

[[See Video to Reveal this Text or Code Snippet]]

This code snippet showcases how to utilize the coerce method effectively within the fuzzing process.

Conclusion: Know When to Fuzz

As we discussed, while fuzzing is invaluable for discovering edge cases, not every scenario necessitates its complexity. In instances where you have well-defined constraints, it can be simpler and more effective to write straightforward unit tests. However, when you do decide to employ fuzzing, understanding how to constrain the inputs will make your testing robust and reliable.

If you have further questions about fuzzing or need additional information on related topics, feel free to ask! Happy coding!