Getting started with fuzzing in Chromium

This document walks you through the basic steps to start fuzzing and suggestions for improving your fuzz targets. If you're looking for more advanced fuzzing topics, see the main page.

Getting started

Setting up your build environment

Generate build files by using the use_libfuzzer GN argument together with a sanitizer:

# AddressSanitizer is the default config we recommend testing with.
# Linux:
tools/mb/ gen -m chromium.fuzz -b 'Libfuzzer Upload Linux ASan' out/libfuzzer
# Chrome OS:
tools/mb/ gen -m chromium.fuzz -b 'Libfuzzer Upload Chrome OS ASan' out/libfuzzer
# Mac:
tools/mb/ gen -m chromium.fuzz -b 'Libfuzzer Upload Mac ASan' out/libfuzzer
# Windows:
python tools\mb\ gen -m chromium.fuzz -b "Libfuzzer Upload Windows ASan" out\libfuzzer
Note: You can also invoke AFL by using the use_afl GN argument, but we recommend libFuzzer for local development. Running libFuzzer locally doesn't require any special configuration and gives quick, meaningful output for speed, coverage, and other parameters.

It’s possible to run fuzz targets without sanitizers, but not recommended, as sanitizers help to detect errors which may not result in a crash otherwise. use_libfuzzer is supported in the following sanitizer configurations.

GN ArgumentDescriptionSupported OS
is_asan=trueEnables AddressSanitizer to catch problems like buffer overruns.Linux, Windows, Mac, Chrome OS
is_msan=trueEnables MemorySanitizer to catch problems like uninitialized reads[*].Linux
is_ubsan_security=trueEnables UndefinedBehaviorSanitizer to catch[*] undefined behavior like integer overflow.Linux

For more on builder and sanitizer configurations, see the Integration Reference page.

Hint: Fuzz targets are built with minimal symbols by default. You can adjust the symbol level by setting the symbol_level attribute.

Creating your first fuzz target

After you set up your build environment, you can create your first fuzz target:

  1. In the same directory as the code you are going to fuzz (or next to the tests for that code), create a new <my_fuzzer>.cc file.

    Note: Do not use the testing/libfuzzer/fuzzers directory. This directory was used for initial sample fuzz targets but is no longer recommended for landing new targets.
  2. In the new file, define a LLVMFuzzerTestOneInput function:

#include <stddef.h>
#include <stdint.h>

extern "C" int LLVMFuzzerTestOneInput(const uint8_t* data, size_t size) {
  // Put your fuzzing code here and use |data| and |size| as input.
  return 0;
  1. In file, define a fuzzer_test GN target:
fuzzer_test("my_fuzzer") {
  sources = [ "" ]
  deps = [ ... ]
Note: Most of the targets are small. They may perform one or a few API calls using the data provided by the fuzzing engine as an argument. However, fuzz targets may be more complex if a certain initialization procedure needs to be performed. is a good example of a complex fuzz target.

Running the fuzz target

After you create your fuzz target, build it with ninja and run it locally:

# Build the fuzz target.
ninja -C out/libfuzzer url_parse_fuzzer
# Create an empty corpus directory.
mkdir corpus
# Run the fuzz target.
./out/libfuzzer/url_parse_fuzzer corpus
# If have other corpus directories, pass their paths as well:
./out/libfuzzer/url_parse_fuzzer corpus seed_corpus_dir_1 seed_corpus_dir_N

Your fuzz target should produce output like this:

INFO: Seed: 1511722356
INFO: Loaded 2 modules   (115485 guards): 22572 [0x7fe8acddf560, 0x7fe8acdf5610), 92913 [0xaa05d0, 0xafb194),
INFO: -max_len is not provided; libFuzzer will not generate inputs larger than 4096 bytes
INFO: A corpus is not provided, starting from an empty corpus
#2  INITED cov: 961 ft: 48 corp: 1/1b exec/s: 0 rss: 48Mb
#3  NEW    cov: 986 ft: 70 corp: 2/104b exec/s: 0 rss: 48Mb L: 103/103 MS: 1 InsertRepeatedBytes-
#4  NEW    cov: 989 ft: 74 corp: 3/106b exec/s: 0 rss: 48Mb L: 2/103 MS: 1 InsertByte-
#6  NEW    cov: 991 ft: 76 corp: 4/184b exec/s: 0 rss: 48Mb L: 78/103 MS: 2 CopyPart-InsertRepeatedBytes-

A ... NEW ... line appears when libFuzzer finds new and interesting inputs. If your fuzz target is efficient, it will find a lot of them quickly. A ... pulse ... line appears periodically to show the current status.

For more information about the output, see libFuzzer's output documentation.

Note: If you observe an odr-violation error in the log, please try setting the following environment variable: ASAN_OPTIONS=detect_odr_violation=0 and running the fuzz target again.

Symbolizing a stacktrace

If your fuzz target crashes when running locally and you see non-symbolized stacktrace, make sure you add the third_party/llvm-build/Release+Asserts/bin/ directory from Chromium’s Clang package in $PATH. This directory contains the llvm-symbolizer binary.

Alternatively, you can set an external_symbolizer_path via the ASAN_OPTIONS environment variable:

ASAN_OPTIONS=external_symbolizer_path=/my/local/llvm/build/llvm-symbolizer \
  ./fuzzer ./crash-input

The same approach works with other sanitizers via MSAN_OPTIONS, UBSAN_OPTIONS, etc.

Submitting your fuzz target

ClusterFuzz and the build infrastructure automatically discover, build and execute all fuzzer_test targets in the Chromium repository. Once you land your fuzz target, ClusterFuzz will run it at scale. Check the ClusterFuzz status page after a day or two.

If you want to better understand and optimize your fuzz target’s performance, see the Efficient Fuzzing Guide.

Note: It’s important to run fuzzers at scale, not just in your own environment, because local fuzzing will catch fewer issues. If you run fuzz targets at scale continuously, you’ll catch regressions and improve code coverage over time.

Optional improvements

Common tricks

Your fuzz target may immediately discover interesting (i.e. crashing) inputs. You can make it more effective with several easy steps:

  • Create a seed corpus. You can guide the fuzzing engine to generate more relevant inputs by adding the seed_corpus = "src/fuzz-testcases/" attribute to your fuzz target and adding example files to the appropriate directory. For more, see the Seed Corpus section of the Efficient Fuzzing Guide.

    Note: make sure your corpus files are appropriately licensed.
  • Create a mutation dictionary. You can make mutations more effective by providing the fuzzer with a dict = "protocol.dict" GN attribute and a dictionary file that contains interesting strings / byte sequences for the target API. For more, see the Fuzzer Dictionary section of the [Efficient Fuzzer Guide].

  • Specify testcase length limits. Long inputs can be problematic, because they are more slowly processed by the fuzz target and increase the search space. By default, libFuzzer uses -max_len=4096 or takes the longest testcase in the corpus if -max_len is not specified.

    ClusterFuzz uses different strategies for different fuzzing sessions, including different random values. Also, ClusterFuzz uses different fuzzing engines (e.g. AFL that doesn't have -max_len option). If your target has an input length limit that you would like to strictly enforce, add a sanity check to the beginning of your LLVMFuzzerTestOneInput function:

    if (size < kMinInputLength || size > kMaxInputLength)
      return 0;
  • Generate a code coverage report. See which code the fuzzer covered in recent runs, so you can gauge whether it hits the important code parts or not.

    Note: Since the code coverage of a fuzz target depends heavily on the corpus provided when running the target, we recommend running the fuzz target built with ASan locally for a little while (several minutes / hours) first. This will produce some corpus, which should be used for generating a code coverage report.

Disabling noisy error message logging

If the code you’re fuzzing generates a lot of error messages when encountering incorrect or invalid data, the fuzz target will be slow and inefficient.

If the target uses Chromium logging APIs, you can silence errors by overriding the environment used for logging in your fuzz target:

struct Environment {
  Environment() {

extern "C" int LLVMFuzzerTestOneInput(const uint8_t* data, size_t size) {
  static Environment env;

  // Put your fuzzing code here and use data+size as input.
  return 0;

Mutating Multiple Inputs

By default, a fuzzing engine such as libFuzzer mutates a single input (uint8_t* data, size_t size). However, APIs often accept multiple arguments of various types, rather than a single buffer. You can use three different methods to mutate multiple inputs at once.

libprotobuf-mutator (LPM)

If you need to mutate multiple inputs of various types and length, see Getting Started with libprotobuf-mutator in Chromium.

Note: This method works with APIs and data structures of any complexity, but requires extra effort. You would need to write a .proto definition (unless you fuzz an existing protobuf) and C++ code to pass the proto message to the API you are fuzzing (you'll have a fuzzed protobuf message instead of data, size buffer).

FuzzedDataProvider (FDP)

FuzzedDataProvider is a class useful for splitting a fuzz input into multiple parts of various types.

Note: FDP is much easier to use than LPM, but its downside is that format of the corpus becomes inconsistent. This doesn‘t matter if you don’t have Seed Corpus (e.g. valid image files if you fuzz an image parser). FDP splits your corpus files into several pieces to fuzz a broader range of input types, so it can take longer to reach deeper code paths that surface more quickly if you fuzz only a single input type.

To use FDP, add #include <fuzzer/FuzzedDataProvider.h> to your fuzz target source file.

To learn more about FuzzedDataProvider, check out the upstream documentation on it. It gives an overview of the available methods and links to a few example fuzz targets.

Hash-based argument

If your API accepts a buffer with data and some integer value (i.e., a bitwise combination of flags), you can calculate a hash value from (data, size) and use it to fuzz an additional integer argument. For example:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t* data, size_t size) {
  std::string str = std::string(reinterpret_cast<const char*>(data), size);
  std::size_t data_hash = std::hash<std::string>()(str);
  APIToBeFuzzed(data, size, data_hash);
  return 0;

Note: The hash method doesn‘t have the corpus format issue mentioned in the FDP section above, but it can lead to results that aren’t as sophisticated as LPM or FDP. The hash value derived from the data is a random value, rather than a meaningful one controlled by the fuzzing engine. A single bit mutation might lead to a new code coverage, but the next mutation would generate a new hash value and trigger another code path, without providing any real guidance to the fuzzing engine.