SharpFuzz: AFL-based fuzz testing for .NET

SharpFuzz is a tool that brings the power of afl-fuzz to .NET platform. If you want to learn more about fuzzing, my motivation for writing SharpFuzz, the types of bugs it can find, or the technical details about how the integration with afl-fuzz works, read my blog post SharpFuzz: Bringing the power of afl-fuzz to .NET platform.

Trophies
Requirements
Installation
Usage
Advanced topics
- Out-of-process fuzzing
- Test case minimization
Acknowledgements

Trophies

If you find some interesting bugs with SharpFuzz, and are comfortable with sharing them, I would love to add them to this list. Please send me an email, make a pull request for the README file, or file an issue.

Requirements

AFL works on Linux and macOS. If you are using Windows, you can use any Linux distribution that works under the Windows Subsystem for Linux.

You will need GNU make and a working compiler (gcc or clang) in order to compile afl-fuzz. You will also need to have the .NET Core 2.1 or greater installed on your machine in order to instrument .NET assemblies with SharpFuzz.

Installation

You can install afl-fuzz and SharpFuzz.CommandLine global .NET tool by running the following script:

#/bin/sh
set -eux

# Download and extract the latest afl-fuzz source package
wget http://lcamtuf.coredump.cx/afl/releases/afl-latest.tgz
tar -xvf afl-latest.tgz

rm afl-latest.tgz
cd afl-2.52b/

# Patch afl-fuzz so that it doesn't check whether the binary
# being fuzzed is instrumented (we have to do this because
# we are going to run our programs with the dotnet run command,
# and the dotnet binary would fail this check)
wget https://github.com/Metalnem/sharpfuzz/raw/master/patches/RemoveInstrumentationCheck.diff
patch < RemoveInstrumentationCheck.diff

# Install afl-fuzz
make install
rm -rf ../afl-2.52b/

# Install SharpFuzz.CommandLine global .NET tool
dotnet tool install --global SharpFuzz.CommandLine --version 1.1.0

Usage

This tutorial assumes that you are somewhat familiar with afl-fuzz. If you don't know anything about it, you should first read the AFL quick start guide and the afl-fuzz README. If you have enough time, I would also recommend reading Understanding the status screen and Technical whitepaper for afl-fuzz.

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 --version 1.1.0

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(() =>
      {
        try
        {
          using (var file = File.OpenText(args[0]))
          {
            JSON.DeserializeDynamic(file);
          }
        }
        catch (DeserializationException) { }
      });
    }
  }
}

We want to fuzz the deserialization capabilities of Jil, which is why we are calling the JSON.DeserializeDynamic method. The path to the input file being tested will always be provided to our program as the first command line argument (afl-fuzz will take care of that during the fuzzing process).

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 \
  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 \
  dotnet bin/Debug/netcoreapp2.1/Fuzzing.dll @@

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 \
  -x /usr/local/share/afl/dictionaries/json.dict \
  dotnet bin/Debug/netcoreapp2.1/Fuzzing.dll @@

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.

Advanced topics

Out-of-process fuzzing

SharpFuzz has several limitations compared to using afl-fuzz directly with native programs. The first one is that if you specify the timeout parameter, and the timeout expires, the whole fuzzing process will be terminated. The second one is that uncatchable exceptions (AccessViolationException and StackOverflowException) will also stop the fuzzing. In both cases, afl-fuzz will terminate and display the following error message:

[-] PROGRAM ABORT : Unable to communicate with fork server (OOM?)
         Location : run_target(), afl-fuzz.c:2405

If you encounter this message during fuzzing, you can recover the input data that has caused the premature exit from the file findings_dir/.cur_input.

There is also an out-of-process version of fuzzer which is using two different .NET processes: the master process for the communication with afl-fuzz, and the child process for the actual fuzzing. If the fuzzing process dies, the master process will just restart it. This comes with the big performance costs if the library you are testing throws a lot of uncatchable exceptions, or timeouts often (starting the new .NET process for each input takes a lot of time), so it's best to fix such bugs as early as possible in order to enjoy the best fuzzing performance. Using the out-of-process fuzzer is as simple as replacing the call to Fuzzer.Run with the call to Fuzzer.OutOfProcess.Run.

Another problem with the out-of-process fuzzer is that the static constructors and all other types of static initialization code are going to run again each time the new child process is started, which will likely negatively affect the trace bits.

Test case minimization

AFL comes with the tool for test case minimization called afl-tmin:

afl-tmin is simple test case minimizer that takes an input file and tries to remove as much data as possible while keeping the binary in a crashing state or producing consistent instrumentation output (the mode is auto-selected based on initially observed behavior).

You can run it using the following command:

afl-tmin -i test_case -o minimized_result \
  dotnet path_to_assembly @@

The only change you have to make in your fuzzing project is to replace the Fuzzer.Run call with the call to Fuzzer.RunOnce.

Acknowledgements

Michal Zalewski - american fuzzy lop
Dmitry Vyukov - go-fuzz: randomized testing for Go
Rody Kersten - Kelinci: AFL-based fuzzing for Java
Jb Evain - Mono.Cecil
0xd4d - dnlib

attackgithub / sharpfuzz Goto Github PK

sharpfuzz's Introduction

SharpFuzz: AFL-based fuzz testing for .NET

Table of contents

Trophies

Requirements

Installation

Usage

Advanced topics

Out-of-process fuzzing

Test case minimization

Acknowledgements

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