Build integration

This page explains how to integrate your project with ClusterFuzzLite’s build system so that ClusterFuzzLite can build your project’s fuzz targets with sanitizers. ClusterFuzzLite is intimately tied to sanitizers and libFuzzer. By integrating with our build system, ClusterFuzzLite will be able to use the most recent versions of these tools to secure your code.

By the end of the document you will be able to build and run your fuzz targets with libFuzzer and a variety of sanitizers.


ClusterFuzzLite supports libFuzzer targets built with Clang on Linux.

ClusterFuzzLite reuses the OSS-Fuzz toolchain to make building easier. This means that ClusterFuzzLite will build your project in a docker container. If you are familiar with OSS-Fuzz, most of the concepts here are exactly the same, with one key difference. Rather than checking out the source code in the Dockerfile using git clone, the Dockerfile copies in the source code directly during docker build. Another minor difference is that ClusterFuzzLite only supports libFuzzer and not other fuzzing engines. If you are not familiar with OSS-Fuzz, have no fear! This document is written with you in mind and assumes no knowledge of OSS-Fuzz.

Before you can start setting up your new project for fuzzing, you must do the following to use the ClusterFuzzLite toolchain:

  • Integrate fuzz targets with your codebase. See this page for more details.

  • Install Docker

    If you want to run docker without sudo, you can create a docker group.

    Note: Docker images can consume significant disk space. Run docker-cleanup periodically to garbage-collect unused images.

  • Clone the OSS-Fuzz repo: git clone

Generating an empty build integration

Next you need to configure your project to build fuzzers on ClusterFuzzLite. To do this, your project needs three configuration files in the .clusterfuzzlite directory in your project’s root:

You can generate empty versions of these files with the following command:

$ cd /path/to/oss-fuzz
$ export PATH_TO_PROJECT=<path_to_your_project>
$ python infra/ generate --external --language=c++ $PATH_TO_PROJECT

Note that you may need to change the --language argument to another value if your project is written in another language. This is discussed more in the language section.

Once the configuration files are generated, you should modify them to fit your project. Let’s look at each file one-by-one and explain what you should add to them.


This configuration file stores project metadata. Currently it is only used by to build your project. The only field you must fill out in this file is:


Programming language the project is written in. Values you can specify include:

  • c
  • c++
  • go
  • rust
  • python
  • jvm
    • This should be used for Java, Kotlin, Scala and other JVM-based languages.
  • swift

Most of this guide applies directly to C/C++ projects. Please see the relevant subguides for how to build fuzzers for that language. Note that c and c++ are the same to ClusterFuzzLite.


This integration file defines the Docker image for building your project. Your script will be executed inside the image this file defines. For most projects, the Dockerfile is simple:

FROM       # Base image with clang toolchain
RUN apt-get update && apt-get install -y ...    # Install required packages to build your project.
COPY . $SRC/<project_name>                      # Copy your project's source code.
WORKDIR $SRC/<project_name>                     # Working directory for
COPY ./.clusterfuzzlite/ $SRC/           # Copy into $SRC dir.

See here for an example.

This script must build binaries for fuzz targets in your project. The script is executed within the image built from your Dockerfile.

In general, this script should do the following:

  • Build the project using your build system with ClusterFuzzLite’s compiler.
  • Provide ClusterFuzzLite’s compiler flags (defined as environment variables) to the build system.
  • Build your fuzz targets and link them with the $LIB_FUZZING_ENGINE (libFuzzer) environment variable.
  • Place any fuzz target binaries in the directory defined by the environment variable $OUT.

Make sure that the binary names for your fuzz targets contain only alphanumeric characters, underscore (_) or dash (-). They should not contain periods (.) or have file extensions. Otherwise, they won’t run. Your should not delete any source code. Source code is needed for code coverage reports.

The $WORK environment variable defines a directory where can store intermediate files.

Here’s an example from Expat:

#!/bin/bash -eu

# configure scripts usually use correct environment variables.

make clean
make -j$(nproc) all

$CXX $CXXFLAGS -std=c++11 -Ilib/ \
    $SRC/ -o $OUT/parse_fuzzer \
    $LIB_FUZZING_ENGINE .libs/libexpat.a

# Optional: Copy dictionaries and options files.
cp $SRC/*.dict $SRC/*.options $OUT/ environment variables for compilation

You must use ClusterFuzzLite’s compilers and compiler flags to build your fuzz targets. These are provided in the following environment variables:

Env Variable Description
$CC, $CXX, $CCC C and C++ compilers.
$CFLAGS, $CXXFLAGS C and C++ compiler flags.
$LIB_FUZZING_ENGINE C++ compiler argument to link fuzz target against libFuzzer.

These compiler flags are needed to properly instrument your fuzzers with sanitizers and coverage instrumentation.

Note that even if your project is written in pure C you must use $CXX to link your fuzz target binaries.

Many build tools will automatically use these environment variables (with the exception of $LIB_FUZZING_ENGINE). If not, pass them manually to the build tool.

You can also do the final linking step with $LIB_FUZZING_ENGINE in your

See the Provided Environment Variables page in OSS-Fuzz’s base-builder image documentation for more details on environment variables that are available to

Fuzzer execution environment

You should not make any assumptions on the availability of dependent packages in the execution environment and the built fuzzers should have dependencies statically linked.

Testing locally

When you have completed writing the and Dockerfile, you should test that they work. This includes running your fuzz targets, which we strongly recommend. The script you used to generate your config files offers a few different ways of doing this:

  1. Build your docker image and fuzz targets:

     $ python infra/ build_image --external $PATH_TO_PROJECT
     $ python infra/ build_fuzzers --external $PATH_TO_PROJECT --sanitizer <address/undefined/memory>

    The built binaries appear in the /path/to/oss-fuzz/build/out/$PROJECT_NAME directory on your host machine (and $OUT in the container). Note that $PROJECT_NAME is the name of the root directory of your project (e.g. if $PATH_TO_PROJECT is /path/to/systemd, $PROJECT_NAME is systemd).

  2. Find common build issues to fix by running the check_build command:

     $ python infra/ check_build --external $PATH_TO_PROJECT --sanitizer <address/undefined/memory>

    This checks that your fuzz targets are compiled with the right sanitizer and don’t crash after fuzzing for a few seconds.

  3. To run a particular fuzz target, use run_fuzzer:

     $ python infra/ run_fuzzer --external --corpus-dir=<path-to-temp-corpus-dir> $PATH_TO_PROJECT <fuzz_target>
  4. If you are going to use the code coverage report feature of ClusterFuzzLite it is a good idea to test that coverage report generation works. This would use the corpus generated from the previous run_fuzzer step in your local corpus directory.

     $ python infra/ build_fuzzers --external --sanitizer coverage $PATH_TO_PROJECT
     $ python infra/ coverage --external $PATH_TO_PROJECT --fuzz-target=<fuzz_target> --corpus-dir=<path-to-temp-corpus-dir>

You may need to run python infra/ pull_images to use the latest coverage tools.

Make sure to test each of the sanitizers with build_fuzzers, check_build, and run_fuzzer.

If everything works locally, it should also work on ClusterFuzzLite. If you experience failures running fuzzers on ClusterFuzzLite, review your dependencies.

Debugging problems

If you run into problems, the Debugging page lists ways to debug your build scripts and fuzz targets.

Efficient fuzzing

To improve your fuzz target ability to find bugs faster, please read this section.

Running ClusterFuzzLite

Next: Step 2: Running ClusterFuzzLite for directions on setting up ClusterFuzzLite to run on your CI.