legacy-usage-instructions.md

As an example, we are going to instrument Jil, which is a fast JSON serializer and deserializer (see SharpFuzz.Samples for many more examples of complete fuzzing projects).

1. Download the package from the NuGet gallery. You can do that by clicking the download package link in the info section of the page. The downloaded file will be called jil.2.16.0.nupkg.

2. Change the extension of the downloaded file from nupkg to zip, and then extract it. The location of the assembly we are going to instrument will be jil.2.16.0/lib/netstandard2.0/Jil.dll. We could have chosen some other .NET platform, such as net45 or netstandard1.6, but the latest version of .NET Standard is usually the best choice.

3. Instrument the assembly by running the sharpfuzz tool with the path to the assembly as a parameter. In our case, the exact command looks like this:

sharpfuzz jil.2.16.0/lib/netstandard2.0/Jil.dll

The instrumentation is performed in place, which means that jil.2.16.0/lib/netstandard2.0/Jil.dll will contain the instrumented version of Jil after running this command.

4. Create a new .NET console project, and add the instrumented library to it, along with all of its dependencies. To do that, copy Jil.dll to the root directory of the project, and then add the following element to your project file:

<ItemGroup>
  <Reference Include="Jil">
    <HintPath>Jil.dll</HintPath>
  </Reference>
</ItemGroup>

Jil depends on Sigil, which is why you also have to manually add the reference to Sigil. You can install it from NuGet with the following command:

dotnet add package Sigil --version 4.7.0

5. Add the SharpFuzz package to the project by running the following command:

dotnet add package SharpFuzz

6. Now it's time to write some code. The Main function should call the SharpFuzz.Fuzzer.Run with the function that we want to test as a parameter. Here's the one possible way we could write this:

using System;
using System.IO;
using SharpFuzz;

namespace Jil.Fuzz
{
  public class Program
  {
    public static void Main(string[] args)
    {
      Fuzzer.Run(stream =>
      {
        try
        {
          using (var reader = new StreamReader(stream))
          {
            JSON.DeserializeDynamic(reader);
          }
        }
        catch (DeserializationException) { }
      });
    }
  }
}

We want to fuzz the deserialization capabilities of Jil, which is why we are calling the JSON.DeserializeDynamic method. The input data will be be provided to us via the stream parameter (if the code you are testing takes its input as a string, you can use an additional overload of Fuzzer.Run that accepts Action<string>).

If the code passed to Fuzzer.Run throws an exception, it will be reported to afl-fuzz as a crash. However, we want to treat only unexpected exceptions as bugs. DeserializationException is what we expect when we encounter an invalid JSON input, which is why we catch it in our example.

7. Create a directory with some test cases (one test is usually more than enough). Test files should contain some input that is accepted by your code as valid, and should also be as small as possible. For example, this is the JSON I'm using for testing JSON deserializers:

{"menu":{"id":1,"val":"X","pop":{"a":[{"click":"Open()"},{"click":"Close()"}]}}}

8. You are now ready to go! Build the project with dotnet build, and start the fuzzing with the following command:

afl-fuzz -i testcases_dir -o findings_dir -t timeout \
  dotnet path_to_assembly

Let's say that our working directory is called Fuzzing. If it contains the project Fuzzing.csproj, and the directory called Testcases, the full command might look like this:

afl-fuzz -i Testcases -o Findings -t 5000 \
  dotnet bin/Debug/netcoreapp2.1/Fuzzing.dll

It's highly recommended that you always specify the timeout (5000ms from the previous example is a good choice), otherwise you will often get false crash reports because AFL uses automatic timeout calculation, which is too sensitive and unsuitable for managed languages.

For formats such as HTML, JavaScript, JSON, or SQL, the fuzzing process can be greatly improved with the usage of a dictionary file. AFL comes with bunch of dictionaries, which you can find after installation in /usr/local/share/afl/dictionaries/. With this in mind, we can improve our fuzzing of Jil like this:

afl-fuzz -i Testcases -o Findings -t 5000 \
  -x /usr/local/share/afl/dictionaries/json.dict \
  dotnet bin/Debug/netcoreapp2.1/Fuzzing.dll

Sometimes you may encounter the following error when running afl-fuzz:

[-] Oops, the program crashed with one of the test cases provided. There are
    several possible explanations:

This usually happens when some of your provided test inputs cause the fuzzing function to throw an exception, but sometimes this can happen due to low default memory limit (I see this very often in the cloud environment). You can fix it by increasing the memory limit for your program to some large value:

afl-fuzz -i testcases_dir -o findings_dir -t 5000 -m 10000 \
  dotnet path_to_assembly

9. Sit back and relax! You will often have some useful results within minutes, but sometimes it can take more than a day, so be patient.

The input files responsible for unhandled exceptions will appear in findings_dir/crashes. The total number of unique crashes will be displayed in red on the afl-fuzz status screen.

In practice, the real number of unique exceptions will often be much lower than the reported number, which is why it's usually best to write a small program that just goes through the crashing inputs, runs the fuzzing function on each of them, and saves only the inputs that produce unique stack traces.