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Adding a new fuzzer

This page explains how to add your fuzzer so it can be benchmarked using FuzzBench. Before you begin make sure you’ve followed the prerequisites section.

Create fuzzer directory

Create a subdirectory under the root fuzzers directory. The name of this subdirectory will be the name FuzzBench uses for your fuzzer. The fuzzer name can contain alphanumeric characters and underscores and must be a valid python module.

export FUZZER_NAME=<your_fuzzer_name>
cd fuzzers
mkdir $FUZZER_NAME

You can verify the name of your fuzzer is valid by running make presubmit from the root of the project.

Create fuzzer files

builder.Dockerfile

This file defines the image that will build your fuzzer and benchmarks for use with your fuzzer. For most projects, this will look like:

ARG parent_image
FROM $parent_image                         # Base builder image (Ubuntu 16.04, with latest Clang).

RUN apt-get update && \                    # Install any system dependencies to build your fuzzer.
    apt-get install -y pkg1 pkg2

RUN git clone <git_url> /fuzzer_src        # Clone your fuzzer's sources.

RUN cd /fuzzer_src && make                 # Build your fuzzer using its preferred build system.

# Build your `FUZZER_LIB`.
# See section below on "What is `FUZZER_LIB`?" for more details.
RUN git clone <git_url> /fuzzer_lib_src

RUN cd /fuzzer_lib_src && clang++ fuzzer_lib.o

Example: afl.

runner.Dockerfile

This file defines the image that will be used to run benchmarks with your fuzzer. Making this lightweight allows trial instances to be spun up fast.

FROM gcr.io/fuzzbench/base-image           # Base image (Ubuntu 16.04).

RUN apt-get update && \                    # Install any runtime dependencies for your fuzzer.
    apt-get install pkg1 pkg2

Example: honggfuzz.

As you can see by looking at the runner.Dockerfile of other projects, in most cases only the FROM line is needed since most fuzzers do not have any special runtime dependencies.

fuzzer.py

This file specifies how to build and fuzz benchmarks using your fuzzer. We hope to have accommodated most common use cases but please file an issue if you’re having trouble.

In your fuzzer directory, create a Python file named fuzzer.py. It must contain two functions:

def build():

A function that accepts no arguments and returns nothing. This function must do two things:

  1. Build the benchmark. This is usually done by setting the necessary environment variables, including CFLAGS, CXXFLAGS, CC, CXX and FUZZER_LIB. Note that CFLAGS and CXXFLAGS should be appended to (using utils.append_flags functions) in most cases, and not set directly (i.e. overwritten).
  2. Copy everything your fuzzer needs to run to the $OUT directory.
def fuzz(input_corpus, output_corpus, target_binary):

A function that accepts three arguments input_corpus, output_corpus and target_binary and returns nothing. fuzz should use these arguments to fuzz the target_binary using your fuzzer.

We have provided an example fuzzer.py with comments explaining the necessary parts below:

import os
import subprocess
import ...  # Import any other python libs you need.

# Helper library that contains important functions for building.
from fuzzers import utils

def build():
    """Build benchmark and copy fuzzer to $OUT."""
    flags = [
        # List of flags to append to CFLAGS, CXXFLAGS during
        # benchmark compilation.
    ]
    utils.append_flags('CFLAGS', flags)     # Adds flags to existing CFLAGS.
    utils.append_flags('CXXFLAGS', flags)   # Adds flags to existing CXXFLAGS.

    os.environ['CC'] = 'clang'              # C compiler.
    os.environ['CXX'] = 'clang++'           # C++ compiler.

    os.environ['FUZZER_LIB'] = 'fuzzer.a'   # Path to your compiled fuzzer lib.

    # Helper function that actually builds benchmarks using the environment you
    # have prepared.
    utils.build_benchmark()

    # You should copy any fuzzer binaries that you need at runtime to the
    # $OUT directory. E.g. for AFL:
    # shutil.copy('/afl/afl-fuzz', os.environ['OUT'])


def fuzz(input_corpus, output_corpus, target_binary):
    """Run fuzzer.

    Arguments:
      input_corpus: Directory containing the initial seed corpus for
                    the benchmark.
      output_corpus: Output directory to place the newly generated corpus
                     from fuzzer run.
      target_binary: Absolute path to the fuzz target binary.
    """
    # Run your fuzzer on the benchmark.
    subprocess.call([
        your_fuzzer,
        '<flag-for-input-corpus>',
        input_corpus,
        '<flag for-output-corpus',
        output_corpus,
        '<other command-line options>',
        target_binary,
    ])

Example: afl.

Environment variables FUZZER and BENCHMARK are available to use during execution of build() and fuzz() functions.

What is FUZZER_LIB?

FUZZER_LIB is a library that gets linked against the benchmark which allows your fuzzer to fuzz the benchmark. For fuzzers like libFuzzer which run entirely in process, FUZZER_LIB is the fuzzer itself. For out-of-process fuzzers like AFL, FUZZER_LIB is a shim that allows them to fuzz our benchmarks which have libFuzzer harnesses. In a libFuzzer harness: fuzzer data is passed to targeted code through a function called LLVMFuzzerTestOneInput:

int LLVMFuzzerTestOneInput(const uint8_t *Data, size_t Size);

Therefore, AFL’s shim takes data from AFL and passes it to LLVMFuzzerTestOneInput.

If, like AFL, your fuzzer has a persistent mode, your FUZZER_LIB should be a library that will call LLVMFuzzerTestOneInput in a loop during fuzzing. For example, in afl’s builder.Dockerfile you can see how afl_driver.cpp is built. In afl’s fuzzer.py this gets used as the FUZZER_LIB.

If your fuzzer does not support persistent mode, you can use the StandAloneFuzzTargetMain.cpp as your FUZZER_LIB. This file takes files as arguments, reads them, and invokes LLVMFuzzerTestOneInput using their data as input (See Eclipser for an example of this). This can be used for a fuzzer that must restart the target after executing each input.

Reusing existing integrations

Most fuzzers, such as FairFuzz are based off other fuzzers such as AFL. In many cases such as these, the derivative fuzzer can simply reuse the original’s integration. For example, FairFuzz’s fuzzer.py imports AFL’s build and fuzz functions and calls them from its own. And its builder.Dockerfile is essentially a copy of AFL’s builder.Dockerfile that simply clones FairFuzz from a different source (Dockerfile functionality should be copied to be reused, inheriting using FROM can’t be used for this purpose).

In the case of AFL, we have tried to write the fuzzer.py file to be modular enough to support different use cases than needing the exact same binaries and fuzzing invocation as AFL. Example: aflplusplus.

Testing it out

Once you’ve got a fuzzer integrated, you should test that it builds and runs successfully:

  • Build a specific benchmark:
export FUZZER_NAME=afl
export BENCHMARK_NAME=libpng-1.2.56
make build-$FUZZER_NAME-$BENCHMARK_NAME
  • Run the fuzzer in the docker image:
make run-$FUZZER_NAME-$BENCHMARK_NAME
  • Or use a quicker test run mode:
make test-run-$FUZZER_NAME-$BENCHMARK_NAME
  • Building all benchmarks for a fuzzer:
make build-$FUZZER_NAME-all

Tips:

  • To debug fuzzer run-time issues, you can either:

    • Start a new shell and run fuzzer there:

      make debug-$FUZZER_NAME-$BENCHMARK_NAME
      
      $ROOT_DIR/docker/benchmark-runner/startup-runner.sh
      
    • Or, debug an existing fuzzer run in the make run-* docker container:

      docker container ls
      docker exec -ti <container-id> /bin/bash
      
      # E.g. check corpus.
      ls /out/corpus
      
  • To do builds in parallel, pass -j to make:

    make -j6 build-$FUZZER_NAME-all
    
  • Run make format to format your code.

  • Run make presubmit to lint your code and ensure all tests are passing.

  • Run make clear-cache to clear docker containers’ caches. Next time you build a project, the container will be built from scratch.

Blocklisting benchmarks

You should make sure that your fuzzer builds and runs successfully against all benchmarks integrated in FuzzBench. This can be done locally using the make test-run-$FUZZER_NAME-$BENCHMARK_NAME command OR you can upload the fuzzer pull request and wait for the CI results.

There can be unavoidable cases where your fuzzer cannot work with a particular benchmark. In those cases, you can add your fuzzer to the unsupported_fuzzers attribute of the benchmark’s benchmark.yaml file. Check out an example here.

Requesting an experiment

The FuzzBench service automatically runs experiments that are requested by users twice a day at 6:00 AM PT (13:00 UTC) and 6:00 PM PT (01:00 UTC). If you want the FuzzBench service to run an experiment on specific fuzzers (such as the one you are adding): add an experiment request to service/experiment-requests.yaml. service/experiment-requests.yaml explains how to do this.

At the end of the experiment, FuzzBench will generate a report comparing your fuzzer to the latest versions of other fuzzers, so you only need to include fuzzers that you’ve modified in a meaningful way (i.e. fuzzers whose results are likely affected by your change).

This report, and a real-time report of your experiment can be viewed at https://www.fuzzbench.com/reports/experimental/$YOUR_EXPERIMENT_NAME (remove the experimental/ directory in path if you are modifying or adding a core fuzzer). Note that real-time reports may not appear until a few hours after the experiment starts since every fuzzer-benchmark pair in the experiment must build in order for fuzzing to start.

Submitting your integration

  • Add your fuzzer to the list in .github/workflows/fuzzers.yml so that our continuous integration will test that your fuzzer can build and briefly run on all benchmarks once you’ve submitted a pull request.

  • Submit the integration in a GitHub pull request.