Link Search Menu Expand Document

Running an experiment

NOTE: Most users of FuzzBench should simply add a fuzzer and use the FuzzBench service. This page isn’t needed for using the FuzzBench service. This page explains how to run an experiment on your own. We don’t recommend running experiments on your own for most users. Validating results from the FuzzBench service is a good reason to run an experiment on your own.

This page assumes a certain level of knowledge about Google Cloud and FuzzBench. If you haven’t already, please follow the guide on setting up a Google Cloud Project to run your own experiments. This page assumes you already have set up a Google Cloud Project, since running an experiment requires Google Cloud.

This page will walk you through on how to use run_experiment.py. Experiments are started by the run_experiment.py script. The script will create a dispatcher instance on Google Compute Engine which runs the experiment, including:

  1. Building desired fuzzer-benchmark combinations.
  2. Starting instances to run fuzzing trials with the fuzzer-benchmark builds and stopping them when they are done.
  3. Measuring the coverage from these trials.
  4. Generating reports based on these measurements.

The rest of this page will assume all commands are run from the root of FuzzBench.

run_experiment.py

Experiment configuration file

You need to create an experiment configuration yaml file. This file contains the configuration parameters for experiments that do not change very often. Below is an example configuration file with explanations of each required parameter.

# The number of trials of a fuzzer-benchmark pair to do.
trials: 5

# The amount of time in seconds that each trial is run for.
# 1 day = 24 * 60 * 60 = 86400
max_total_time: 86400

# The docker registry for your fuzzbench experiments.
# If you use Google Cloud, this can be gcr.io/$PROJECT_NAME.
docker_registry: gcr.io/$PROJECT_NAME

# The name of your Google Cloud project.
cloud_project: $PROJECT_NAME

# The Google Compute Engine zone to run the experiment in.
cloud_compute_zone: $PROJECT_ZONE

# The Google Cloud Storage bucket that will store most of the experiment data.
experiment_filestore: gs://$DATA_BUCKET_NAME

# The Google Cloud Storage bucket where HTML reports and summary data will be stored.
report_filestore: gs://$REPORT_BUCKET_NAME

# The connection to use to connect to the Google Cloud SQL instance.
cloud_sql_instance_connection_name: "$PROJECT_NAME:$PROJECT_REGION:$POSTGRES_INSTANCE=tcp:5432"

NOTE: The values $PROJECT_NAME, $PROJECT_REGION, PROJECT_ZONE, $DATA_BUCKET_NAME, $REPORT_BUCKET_NAME $POSTGRES_INSTANCE refer to the values of those environment variables that were set in the guide on setting up a Google Cloud Project. For example if $PROJECT_NAME is my-fuzzbench-project, use my-fuzzbench-project and not $PROJECT_NAME.

Setting the database password

Find the password for the PostgreSQL instance you are using in your experiment config. Set it using the environment variable POSTGRES_PASSWORD like so:

export POSTGRES_PASSWORD="my-super-secret-password"

Benchmarks

Pick the benchmarks you want to use from the benchmarks/ directory.

For example: freetype2-2017 and bloaty_fuzz_target.

Fuzzers

Pick the fuzzers you want to use from the fuzzers/ directory. For example: libfuzzer and afl.

Executing run_experiment.py

Now that everything is ready, execute run_experiment.py:

PYTHONPATH=. python3 experiment/run_experiment.py \
--experiment-config experiment-config.yaml \
--benchmarks freetype2-2017 bloaty_fuzz_target \
--experiment-name $EXPERIMENT_NAME \
--fuzzers afl libfuzzer

where $EXPERIMENT_NAME is the name you want to give the experiment.

You can optionally add:

  • --no-seeds - to skip using seed corpus across all benchmarks.
  • --no-dictionaries - to skip using dictionaries across all benchmarks.
  • --oss-fuzz-corpus - use the latest corpora from OSS-Fuzz across all benchmarks (where available).

Viewing reports

You should eventually be able to see reports from your experiment, that are update at some interval throughout the experiment. However, you may have to wait a while until they first appear since a lot must happen before there is data to generate report. Once they are available, you should be able to view them at: https://storage.googleapis.com/$REPORT_BUCKET_NAME/$EXPERIMENT_NAME/index.html

Advanced usage

Fuzzer configuration files

Many fuzzers have knobs that affect performance. To make it easier to tweak these knobs in experiments run_experiment.py supports fuzzer configuration files. To use a “configured” fuzzer in an experiment, pass the configuration file to run_experiment.py using the --fuzzer-configs argument. Below is an example configuration file with an explanation of how it can configure a fuzzer.

# The name of the fuzzer in fuzzers/ we want to run.
fuzzer: libfuzzer

# The name that we want to use for this configuration (e.g. results for this
# configuration show up in reports under the name "libfuzzer_value_profile")
variant_name: libfuzzer_value_profile

# Environment variables that are set before running the fuzzer's fuzzer.py
# script. Note that these have no meaning to fuzzbench, it's up to fuzzer.py
# to do something with them. See fuzzers/libfuzzer/fuzzer.py
# (https://github.com/google/fuzzing/blob/master/fuzzers/libfuzzer/fuzzer.py)
# for how ADDITIONAL_ARGS is used.
env:
  ADDITIONAL_ARGS: -use_value_profile=1

Currently values in env are only set before running the fuzzer, not before building the benchmarks or the fuzzer itself.