PoC: Fuzz Testing

[sebastian-sommerfeld-io]

As the maintainer of the source2adoc CLI tool, I want to implement a proof of concept (PoC) for fuzz testing to ensure the robustness and reliability of my application. The goal is to identify and address potential vulnerabilities, unexpected behaviors, and edge cases by automatically generating and testing a wide range of input scenarios. This PoC will help enhance the overall stability and security of the source2adoc tool, ensuring it performs consistently under various conditions.

See

• https://go.dev/doc/security/fuzz/
• https://go.dev/doc/tutorial/fuzz

Acceptance Criteria

• [ ] :zap: Scenario: Name
  • :zap: Given ...
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• [ ] :zap: Scenario: Name
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:zap: :zap: Maybe my Fuzz tests are the Acceptance Criteria this time? :zap: :zap: :zap: :zap: How can I run Fuzz Tests in a pipeline? :zap: :zap:

[sebastian-sommerfeld-io]

:speech_balloon: Conversation Transcript from talking to ChatGPT

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User:

What is fuzzing?

ChatGPT:

Fuzzing, or fuzz testing, is a software testing technique used to discover vulnerabilities, bugs, and security issues in software systems by providing them with invalid, unexpected, or random data inputs. The goal of fuzzing is to identify unexpected behaviors, such as crashes, memory leaks, or security vulnerabilities, that can occur when the software is exposed to abnormal inputs.

How Fuzzing Works:

1. Input Generation: Fuzzers generate a wide variety of inputs, often random or semi-random, that deviate from what the software typically expects. These inputs can range from slight deviations to completely malformed data.
2. Input Injection: The generated inputs are then fed into the target software or system to see how it handles them.
3. Monitoring: While the software processes these inputs, the fuzzer monitors its behavior. This includes tracking crashes, hangs, or other anomalous behaviors that might indicate a problem.
4. Analysis and Reporting: If the fuzzer detects an issue, it logs the input that caused the problem along with relevant details. This information helps developers analyze and fix the underlying issue.

Types of Fuzzing:

• Black-box Fuzzing: The fuzzer generates inputs without any knowledge of the internal workings of the software. It treats the software as a "black box" and focuses on how it behaves externally.
• White-box Fuzzing: The fuzzer has knowledge of the software's internal structure and uses this information to generate more targeted and effective inputs.
• Grey-box Fuzzing: A combination of black-box and white-box approaches, where the fuzzer has some knowledge of the internal structure but not complete access.

Applications of Fuzzing:

• Security Testing: Fuzzing is widely used in security testing to uncover vulnerabilities like buffer overflows, memory corruption, and other security flaws.
• Software Quality Assurance: Beyond security, fuzzing helps in identifying bugs and stability issues in software.
• Protocol Testing: Fuzzing can be used to test the robustness of network protocols by sending malformed packets.

Advantages of Fuzzing:

• Automated and Scalable: Fuzzing can be automated, allowing for large-scale testing with minimal human intervention.
• Effective at Finding Bugs: It can uncover vulnerabilities that are difficult to find using traditional testing methods.

Limitations:

• False Positives/Negatives: Not all inputs that cause crashes are relevant, and some vulnerabilities may not be detected.
• Complexity in Setup: Configuring an effective fuzzer can be complex, especially for sophisticated systems.

Overall, fuzzing is a powerful tool for enhancing software security and reliability by identifying and addressing issues before they can be exploited or cause failures.

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User:

I have a CLI app in a Docker image. The app can be used like this: docker run --volume "$(pwd):$(pwd)" --workdir "$(pwd)" sommerfeldio/source2adoc:latest --source-dir ./src --output-dir ./docs Can I use fuzz testing for this app? What is the upside?

ChatGPT:

Yes, you can use fuzz testing for your CLI application running in a Docker image, and there are several potential benefits to doing so.

How to Fuzz Test Your CLI App in Docker:

Given that your app takes specific arguments (--source-dir and --output-dir), you can apply fuzz testing by generating various kinds of inputs for these arguments and observing how your application handles them.

1. Fuzzing Input Arguments:

   • Source Directory (--source-dir): Generate different types of directory paths, including valid paths, invalid paths, paths with special characters, very long paths, and empty strings.
   • Output Directory (--output-dir): Similar to the source directory, you can fuzz test this argument with a range of path values.
   • Additional Inputs: If your CLI accepts file names or content within the source directory, you can fuzz these files by generating random or malformed file content to see how the application handles different scenarios.

2. Automating the Fuzzing Process:

   • Fuzzing Tools: Use fuzzing tools like AFL (American Fuzzy Lop) or libFuzzer that can generate input cases automatically. You can create a script that runs your Docker command with different generated inputs.
   • Monitoring: The fuzzer should monitor the output for crashes, hangs, or other anomalous behavior. Given that you're running the app in a Docker container, monitoring can also include checking Docker logs, exit codes, and any error messages.

3. Handling Docker Specifics:

   • Since your app is containerized, ensure that the fuzzer can interact with the Docker environment. You might want to automate the fuzz testing within the Docker context using a script that repeatedly runs the Docker command with different inputs.

Upsides of Fuzz Testing Your CLI App:

1. Uncover Hidden Bugs: Fuzz testing can reveal edge cases that manual testing might miss, such as handling of unexpected or malformed input, unexpected directory structures, or race conditions.
2. Enhance Security: By identifying vulnerabilities that could be exploited, such as path traversal issues, directory permissions problems, or input validation flaws, fuzz testing helps improve the security of your application.
3. Improve Robustness: Regular input validation bugs, unexpected behavior with different file types or sizes, or issues with error handling are often caught during fuzz testing, making your application more resilient.
4. Automated and Scalable: Fuzzing is an automated process that can run continually or as part of a CI/CD pipeline, allowing for large-scale testing without needing extensive manual intervention.

Example Scenario:

Suppose your CLI app is designed to convert files from a source directory to a documentation format in the output directory. Fuzz testing could involve:

• Providing corrupted or unusual file formats in --source-dir.
• Specifying --output-dir as a location with insufficient permissions.
• Using extremely long or deeply nested paths for --source-dir or --output-dir.
• Testing edge cases like an empty directory, very large directories, or symbolic links.

These tests can reveal how your application handles unexpected scenarios and can lead to improvements in error handling, logging, and overall stability.

Conclusion:

Fuzz testing your CLI app in Docker can be very beneficial, especially for enhancing security, robustness, and uncovering hidden bugs. Given that your application processes directory paths and potentially file content, fuzz testing can help ensure that your app behaves correctly even in the face of unexpected or malicious inputs.

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User:

Can I implement fuzz tests with Go? Maybe even GoDog?

ChatGPT:

Yes, you can implement fuzz tests in Go, and while GoDog (a BDD framework for Go) is not directly a fuzz testing tool, it can complement your testing strategy by handling behavior-driven tests. Here's how you can use Go for fuzz testing, potentially alongside GoDog:

Fuzz Testing in Go:

Go has native support for fuzz testing starting with Go 1.18, which makes it easy to integrate fuzz testing into your Go projects.

Using GoDog for Behavior-Driven Testing:

GoDog is a behavior-driven development (BDD) tool for Go, inspired by Cucumber. It is not a fuzzing tool, but it can be used alongside fuzz testing to define and verify specific expected behaviors of your CLI app.

Combining Fuzz Testing and GoDog:

While fuzz testing and BDD testing are different in nature, they complement each other:

• Fuzz Testing: Use it to discover edge cases, unexpected inputs, and potential vulnerabilities in your CLI app.
• GoDog (BDD): Use it to define and verify that specific, well-defined behaviors and user scenarios work as expected.

By using fuzz testing in Go to stress-test your application and GoDog to validate expected behaviors, you create a robust testing suite that ensures both the correctness and resilience of your CLI application.

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User:

Can I use fuzz tests in Go out of the box?

ChatGPT:

Yes, you can use fuzz testing in Go "out of the box" starting from Go version 1.18. Go's standard testing package includes built-in support for fuzz testing, so you don't need to install any additional libraries or tools to get started.

Steps to Use Fuzz Testing in Go:

1. Ensure You Have Go 1.18 or Later: Fuzz testing is only available in Go versions 1.18 and later.
2. Write a Fuzz Test: Fuzz tests in Go are written similarly to normal unit tests but are prefixed with Fuzz.
3. Run the Fuzz Test: Run the fuzz test using the go test command with the -fuzz flag. Go will generate various inputs and continue testing until it finds a failure or is manually stopped.
4. Analyze the Results: If the fuzz test finds a failure, Go will report it and provide the input that caused the issue. You can then use this input to reproduce and debug the problem.

Benefits of Go's Built-In Fuzz Testing:

• Ease of Use: No need for additional tools or libraries; fuzz testing is built directly into the standard testing package.
• Integration: Fuzz tests integrate seamlessly with Go's existing testing framework, allowing you to run them alongside your unit tests.
• Automated Input Generation: The Go testing framework automatically handles the generation and mutation of inputs, reducing the manual effort required to test edge cases.

Conclusion: Go's built-in fuzz testing feature, introduced in Go 1.18, allows you to start fuzz testing your code with minimal setup. By leveraging this functionality, you can efficiently uncover edge cases, improve the robustness of your application, and identify potential security vulnerabilities.