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 fuzzer files
- Testing it out
- Blocklisting benchmarks
- Requesting an experiment
- Submitting your integration
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.
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
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
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.
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:
A function that accepts no arguments and returns nothing. This function must do two things:
- Build the benchmark. This is usually done by setting the necessary environment variables, including
FUZZER_LIB. Note that
CXXFLAGSshould be appended to (using
utils.append_flagsfunctions) in most cases, and not set directly (i.e. overwritten).
- Copy everything your fuzzer needs to run to the
def fuzz(input_corpus, output_corpus, target_binary):
A function that accepts three arguments
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, ])
BENCHMARK are available to use during execution of
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
int LLVMFuzzerTestOneInput(const uint8_t *Data, size_t Size);
Therefore, AFL’s shim takes data from AFL and passes it to
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
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.
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
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.
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-$BENCHMARK_NAME
- To debug a build:
And then run
fuzzer_build when you have a shell on the builder.
- Run the fuzzer in the docker image:
- Or use a quicker test run mode:
- Building all benchmarks for a fuzzer:
To debug fuzzer run-time issues, you can either:
Start a new shell and run fuzzer there:
make debug-$FUZZER_NAME-$BENCHMARK_NAME $ROOT_DIR/docker/benchmark-runner/startup-runner.sh
Or, debug an existing fuzzer run in the
make run-*docker container:
docker container ls docker exec -ti <container-id> /bin/bash # E.g. check corpus. ls /out/corpus
To do builds in parallel, pass -j
make -j6 build-$FUZZER_NAME-all
make formatto format your code.
make presubmitto lint your code and ensure all tests are passing.
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.
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.
Add your fuzzer to the list in
.github/workflows/fuzzers.ymlso 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.