Efficient Fuzzer

This document describes ways to determine your fuzzer efficiency and ways to improve it.


Being a coverage-driven fuzzer, libFuzzer considers a certain input interesting if it results in new coverage. The set of all interesting inputs is called corpus. Items in corpus are constantly mutated in search of new interesting input. Corpus is usually maintained between multiple fuzzer runs.

Following things can be so effective for a fuzzer, that we strongly recommend for any fuzzer:

There are several metrics you should look at to determine your fuzzer effectiveness:

You can collect these metrics manually or take them from ClusterFuzz status pages.

Seed Corpus

You can pass a corpus directory to a fuzzer that you run manually:

./out/libfuzzer/my_fuzzer ~/tmp/my_fuzzer_corpus

The directory can initially be empty. The fuzzer would store all the interesting items it finds in the directory. You can help the fuzzer by “seeding” the corpus: simply copy interesting inputs for your function to the corpus directory before running. This works especially well for strictly defined file formats or data transmission protocols.

  • For file-parsing functionality just use some valid files from your test suite.

  • For protocol processing targets put raw streams from test suite into separate files.

ClusterFuzz uses seed corpus stored in Chromium repository. You need to add seed_corpus attribute to fuzzer target:

fuzzer_test("my_protocol_fuzzer") {
  seed_corpus = "src/fuzz/testcases"

You may specify multiple seed corpus directories via seed_corpuses attribute:

fuzzer_test("my_protocol_fuzzer") {
  seed_corpuses = [ "src/fuzz/testcases", "src/unittest/data" ]

All files found in the directories and their subdirectories will be archived into %YOUR_FUZZER_NAME%_seed_corpus.zip output archive.

If you don't want to store seed corpus in Chromium repository, you can upload corpus to Google Cloud Storage bucket used by ClusterFuzz:

  1. go to Corpus GCS Bucket

  2. open directory named %YOUR_FUZZER_NAME%_static

  3. upload corpus files into the directory

Alternative way is to use gsutil tool:

gsutil -m rsync <corpus_dir_on_disk> gs://clusterfuzz-corpus/libfuzzer/%YOUR_FUZZER_NAME%_static

Fuzzer Dictionary

It is very useful to provide fuzzer a set of common words/values that you expect to find in the input. This greatly improves efficiency of finding new units and works especially well while fuzzing file format decoders.

To add a dictionary, first create a dictionary file. Dictionary syntax is similar to that used by AFL for its -x option:

# Lines starting with '#' and empty lines are ignored.

# Adds "blah" (w/o quotes) to the dictionary.
# Use \\ for backslash and \" for quotes.
# Use \xAB for hex values
# the name of the keyword followed by '=' may be omitted:

Test your dictionary by running your fuzzer locally:

./out/libfuzzer/my_protocol_fuzzer -dict=<path_to_dict> <path_to_corpus>

You should see lots of new units discovered.

Add dict attribute to fuzzer target:

fuzzer_test("my_protocol_fuzzer") {
  dict = "protocol.dict"

Make sure to submit dictionary file to git. The dictionary will be used automatically by ClusterFuzz once it picks up new fuzzer version (once a day).

Fuzzer Speed

Fuzzer speed is printed while fuzzer runs:

#19346  NEW    cov: 2815 bits: 1082 indir: 43 units: 150 exec/s: 19346 L: 62

Because libFuzzer performs randomized search, it is critical to have it as fast as possible. You should try to get to at least 1,000 exec/s. Profile the fuzzer using any standard tool to see where it spends its time.


Try to keep your fuzzing function as simple as possible. Prefer to use static initialization and shared resources rather than bringing environment up and down every single run.

Fuzzers don't have to shutdown gracefully (we either kill them or they crash because sanitizer has found a problem). You can skip freeing static resource.

Of course all resources allocated within LLVMFuzzerTestOneInput function should be deallocated since this function is called millions of times during one fuzzing session.

Memory Usage

Avoid allocation of dynamic memory wherever possible. Instrumentation works faster for stack-based and static objects than for heap allocated ones.

It is always a good idea to play with different versions of a fuzzer to find the fastest implementation.

Maximum Testcase Length

Experiment with different values of -max_len parameter. This parameter often significantly affects execution speed, but not always.

  1. Define which -max_len value is reasonable for your target. For example, it may be useless to fuzz an image decoder with too small value of testcase length.

  2. Increase the value defined on previous step. Check its influence on execution speed of fuzzer. If speed doesn't drop significantly for long inputs, it is fine to have some bigger value for -max_len.

In general, bigger -max_len value gives better coverage. Coverage is main priority for fuzzing. However, low execution speed may result in waste of resources used for fuzzing. If large inputs make fuzzer too slow you have to adjust value of -max_len and find a trade-off between coverage and execution speed.

Note: ClusterFuzz runs two different fuzzing engines (LibFuzzer and AFL) using the same target functions. AFL doesn‘t support -max_len parameter and may provide input of any length to the target. If your target has an input length limit that you would like to strictly enforce, it’s recommended to add a sanity check to the beginning of your target function:

if (size > kSizeLimit)
  return 0;

For more information check out the discussion in issue 638836.


ClusterFuzz status page provides fuzzer source-level coverage report from the recent run. Looking at the report might provide an insight to improve fuzzer coverage.

You can also access source-level coverage report locally:

# produces binary .sancov file
ASAN_OPTIONS=coverage=1 ./out/libfuzzer/my_fuzzer -runs=0 ~/tmp/my_fuzzer_corpus
# Convert binary .sancov to symbolized .symcov file.
./third_party/llvm-build/Release+Asserts/bin/sancov \
  -symbolize my_fuzzer my_fuzzer.123.sancov > my_fuzzer.symcov
# Launch coverage report server
curl https://llvm.org/svn/llvm-project/llvm/trunk/tools/sancov/coverage-report-server.py | python3 \
  --symcov my_fuzzer.symcov --srcpath path_to_chromium_sources
# Navigate to http://localhost:8001/ to view coverage report

Replace ASAN_OPTIONS by corresponding option variable if your are using another sanitizer (e.g. MSAN_OPTIONS).

NOTE: This is an experimental feature and an active area of work. We are working on improving this process.

Corpus Size

After running for a while the fuzzer would reach a plateau and won't discover new interesting input. Corpus for a reasonably complex functionality should contain hundreds (if not thousands) of items.

Too small corpus size indicates some code barrier that libFuzzer is having problems penetrating. Common cases include: checksums, magic numbers etc. The easiest way to diagnose this problem is to generate a coverage report. To fix the issue you can:

Custom Options

It is possible to specify libFuzzer parameters for any fuzzer being run at ClusterFuzz. Custom options will overwrite default values provided by ClusterFuzz.

Just list all parameters in libfuzzer_options variable of build target:

fuzzer_test("my_protocol_fuzzer") {
  libfuzzer_options = [

Please note that dict parameter should be provided separately. Other options may be passed through libfuzzer_options property.