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LibFuzzer -- a library for coverage-guided fuzz testing.

Introduction

This library is intended primarily for in-process coverage-guided fuzz testing (fuzzing) of other libraries. The typical workflow looks like this:

  • Build the Fuzzer library as a static archive (or just a set of .o files). Note that the Fuzzer contains the main() function. Preferably do not use sanitizers while building the Fuzzer.
  • Build the library you are going to test with -fsanitize-coverage={bb,edge}[,indirect-calls,8bit-counters] and one of the sanitizers. We recommend to build the library in several different modes (e.g. asan, msan, lsan, ubsan, etc) and even using different optimizations options (e.g. -O0, -O1, -O2) to diversify testing.
  • Build a test driver using the same options as the library. The test driver is a C/C++ file containing interesting calls to the library inside a single function extern "C" void LLVMFuzzerTestOneInput(const uint8_t *Data, size_t Size);
  • Link the Fuzzer, the library and the driver together into an executable using the same sanitizer options as for the library.
  • Collect the initial corpus of inputs for the fuzzer (a directory with test inputs, one file per input). The better your inputs are the faster you will find something interesting. Also try to keep your inputs small, otherwise the Fuzzer will run too slow. By default, the Fuzzer limits the size of every input to 64 bytes (use -max_len=N to override).
  • Run the fuzzer with the test corpus. As new interesting test cases are discovered they will be added to the corpus. If a bug is discovered by the sanitizer (asan, etc) it will be reported as usual and the reproducer will be written to disk. Each Fuzzer process is single-threaded (unless the library starts its own threads). You can run the Fuzzer on the same corpus in multiple processes in parallel.

The Fuzzer is similar in concept to AFL, but uses in-process Fuzzing, which is more fragile, more restrictive, but potentially much faster as it has no overhead for process start-up. It uses LLVM's SanitizerCoverage instrumentation to get in-process coverage-feedback

The code resides in the LLVM repository, requires the fresh Clang compiler to build and is used to fuzz various parts of LLVM, but the Fuzzer itself does not (and should not) depend on any part of LLVM and can be used for other projects w/o requiring the rest of LLVM.

Flags

The most important flags are:

seed                                  0       Random seed. If 0, seed is generated.
runs                                  -1      Number of individual test runs (-1 for infinite runs).
max_len                               64      Maximum length of the test input.
cross_over                            1       If 1, cross over inputs.
mutate_depth                          5       Apply this number of consecutive mutations to each input.
timeout                               1200    Timeout in seconds (if positive). If one unit runs more than this number of seconds the process will abort.
help                                  0       Print help.
save_minimized_corpus                 0       If 1, the minimized corpus is saved into the first input directory
jobs                                  0       Number of jobs to run. If jobs >= 1 we spawn this number of jobs in separate worker processes with stdout/stderr redirected to fuzz-JOB.log.
workers                               0       Number of simultaneous worker processes to run the jobs. If zero, "min(jobs,NumberOfCpuCores()/2)" is used.
tokens                                0       Use the file with tokens (one token per line) to fuzz a token based input language.
apply_tokens                          0       Read the given input file, substitute bytes  with tokens and write the result to stdout.
sync_command                          0       Execute an external command "<sync_command> <test_corpus>" to synchronize the test corpus.
sync_timeout                          600     Minimum timeout between syncs.

For the full list of flags run the fuzzer binary with -help=1.

Usage examples

Toy example

A simple function that does something interesting if it receives the input "HI!":

cat << EOF >> test_fuzzer.cc
extern "C" void LLVMFuzzerTestOneInput(const unsigned char *data, unsigned long size) {
  if (size > 0 && data[0] == 'H')
    if (size > 1 && data[1] == 'I')
       if (size > 2 && data[2] == '!')
       __builtin_trap();
}
EOF
# Get lib/Fuzzer. Assuming that you already have fresh clang in PATH.
svn co http://llvm.org/svn/llvm-project/llvm/trunk/lib/Fuzzer
# Build lib/Fuzzer files.
clang -c -g -O2 -std=c++11 Fuzzer/*.cpp -IFuzzer
# Build test_fuzzer.cc with asan and link against lib/Fuzzer.
clang++ -fsanitize=address -fsanitize-coverage=edge test_fuzzer.cc Fuzzer*.o
# Run the fuzzer with no corpus.
./a.out

You should get Illegal instruction (core dumped) pretty quickly.

PCRE2

Here we show how to use lib/Fuzzer on something real, yet simple: pcre2:

COV_FLAGS=" -fsanitize-coverage=edge,indirect-calls,8bit-counters"
# Get PCRE2
svn co svn://vcs.exim.org/pcre2/code/trunk pcre
# Get lib/Fuzzer. Assuming that you already have fresh clang in PATH.
svn co http://llvm.org/svn/llvm-project/llvm/trunk/lib/Fuzzer
# Build PCRE2 with AddressSanitizer and coverage.
(cd pcre; ./autogen.sh; CC="clang -fsanitize=address $COV_FLAGS" ./configure --prefix=`pwd`/../inst && make -j && make install)
# Build lib/Fuzzer files.
clang -c -g -O2 -std=c++11 Fuzzer/*.cpp -IFuzzer
# Build the actual function that does something interesting with PCRE2.
cat << EOF > pcre_fuzzer.cc
#include <string.h>
#include "pcre2posix.h"
extern "C" void LLVMFuzzerTestOneInput(const unsigned char *data, size_t size) {
  if (size < 1) return;
  char *str = new char[size+1];
  memcpy(str, data, size);
  str[size] = 0;
  regex_t preg;
  if (0 == regcomp(&preg, str, 0)) {
    regexec(&preg, str, 0, 0, 0);
    regfree(&preg);
  }
  delete [] str;
}
EOF
clang++ -g -fsanitize=address $COV_FLAGS -c -std=c++11  -I inst/include/ pcre_fuzzer.cc
# Link.
clang++ -g -fsanitize=address -Wl,--whole-archive inst/lib/*.a -Wl,-no-whole-archive Fuzzer*.o pcre_fuzzer.o -o pcre_fuzzer

This will give you a binary of the fuzzer, called pcre_fuzzer. Now, create a directory that will hold the test corpus:

mkdir -p CORPUS

For simple input languages like regular expressions this is all you need. For more complicated inputs populate the directory with some input samples. Now run the fuzzer with the corpus dir as the only parameter:

./pcre_fuzzer ./CORPUS

You will see output like this:

Seed: 1876794929
#0      READ   cov 0 bits 0 units 1 exec/s 0
#1      pulse  cov 3 bits 0 units 1 exec/s 0
#1      INITED cov 3 bits 0 units 1 exec/s 0
#2      pulse  cov 208 bits 0 units 1 exec/s 0
#2      NEW    cov 208 bits 0 units 2 exec/s 0 L: 64
#3      NEW    cov 217 bits 0 units 3 exec/s 0 L: 63
#4      pulse  cov 217 bits 0 units 3 exec/s 0
  • The Seed: line shows you the current random seed (you can change it with -seed=N flag).
  • The READ line shows you how many input files were read (since you passed an empty dir there were inputs, but one dummy input was synthesised).
  • The INITED line shows you that how many inputs will be fuzzed.
  • The NEW lines appear with the fuzzer finds a new interesting input, which is saved to the CORPUS dir. If multiple corpus dirs are given, the first one is used.
  • The pulse lines appear periodically to show the current status.

Now, interrupt the fuzzer and run it again the same way. You will see:

Seed: 1879995378
#0      READ   cov 0 bits 0 units 564 exec/s 0
#1      pulse  cov 502 bits 0 units 564 exec/s 0
...
#512    pulse  cov 2933 bits 0 units 564 exec/s 512
#564    INITED cov 2991 bits 0 units 344 exec/s 564
#1024   pulse  cov 2991 bits 0 units 344 exec/s 1024
#1455   NEW    cov 2995 bits 0 units 345 exec/s 1455 L: 49

This time you were running the fuzzer with a non-empty input corpus (564 items). As the first step, the fuzzer minimized the set to produce 344 interesting items (the INITED line)

It is quite convenient to store test corpuses in git. As an example, here is a git repository with test inputs for the above PCRE2 fuzzer:

git clone https://github.com/kcc/fuzzing-with-sanitizers.git
./pcre_fuzzer ./fuzzing-with-sanitizers/pcre2/C1/

You may run N independent fuzzer jobs in parallel on M CPUs:

N=100; M=4; ./pcre_fuzzer ./CORPUS -jobs=$N -workers=$M

By default (-reload=1) the fuzzer processes will periodically scan the CORPUS directory and reload any new tests. This way the test inputs found by one process will be picked up by all others.

If -workers=$M is not supplied, min($N,NumberOfCpuCore/2) will be used.

Heartbleed

Remember Heartbleed? As it was recently shown, fuzzing with AddressSanitizer can find Heartbleed. Indeed, here are the step-by-step instructions to find Heartbleed with LibFuzzer:

wget https://www.openssl.org/source/openssl-1.0.1f.tar.gz
tar xf openssl-1.0.1f.tar.gz
COV_FLAGS="-fsanitize-coverage=edge,indirect-calls" # -fsanitize-coverage=8bit-counters
(cd openssl-1.0.1f/ && ./config &&
  make -j 32 CC="clang -g -fsanitize=address $COV_FLAGS")
# Get and build LibFuzzer
svn co http://llvm.org/svn/llvm-project/llvm/trunk/lib/Fuzzer
clang -c -g -O2 -std=c++11 Fuzzer/*.cpp -IFuzzer
# Get examples of key/pem files.
git clone   https://github.com/hannob/selftls
cp selftls/server* . -v
cat << EOF > handshake-fuzz.cc
#include <openssl/ssl.h>
#include <openssl/err.h>
#include <assert.h>
SSL_CTX *sctx;
int Init() {
  SSL_library_init();
  SSL_load_error_strings();
  ERR_load_BIO_strings();
  OpenSSL_add_all_algorithms();
  assert (sctx = SSL_CTX_new(TLSv1_method()));
  assert (SSL_CTX_use_certificate_file(sctx, "server.pem", SSL_FILETYPE_PEM));
  assert (SSL_CTX_use_PrivateKey_file(sctx, "server.key", SSL_FILETYPE_PEM));
  return 0;
}
extern "C" void LLVMFuzzerTestOneInput(unsigned char *Data, size_t Size) {
  static int unused = Init();
  SSL *server = SSL_new(sctx);
  BIO *sinbio = BIO_new(BIO_s_mem());
  BIO *soutbio = BIO_new(BIO_s_mem());
  SSL_set_bio(server, sinbio, soutbio);
  SSL_set_accept_state(server);
  BIO_write(sinbio, Data, Size);
  SSL_do_handshake(server);
  SSL_free(server);
}
EOF
# Build the fuzzer.
clang++ -g handshake-fuzz.cc  -fsanitize=address \
  openssl-1.0.1f/libssl.a openssl-1.0.1f/libcrypto.a Fuzzer*.o
# Run 20 independent fuzzer jobs.
./a.out  -jobs=20 -workers=20

Voila:

#1048576        pulse  cov 3424 bits 0 units 9 exec/s 24385
=================================================================
==17488==ERROR: AddressSanitizer: heap-buffer-overflow on address 0x629000004748 at pc 0x00000048c979 bp 0x7fffe3e864f0 sp 0x7fffe3e85ca8
READ of size 60731 at 0x629000004748 thread T0
    #0 0x48c978 in __asan_memcpy
    #1 0x4db504 in tls1_process_heartbeat openssl-1.0.1f/ssl/t1_lib.c:2586:3
    #2 0x580be3 in ssl3_read_bytes openssl-1.0.1f/ssl/s3_pkt.c:1092:4

Advanced features

Tokens

By default, the fuzzer is not aware of complexities of the input language and when fuzzing e.g. a C++ parser it will mostly stress the lexer. It is very hard for the fuzzer to come up with something like reinterpret_cast<int> from a test corpus that doesn't have it. See a detailed discussion of this topic at http://lcamtuf.blogspot.com/2015/01/afl-fuzz-making-up-grammar-with.html.

lib/Fuzzer implements a simple technique that allows to fuzz input languages with long tokens. All you need is to prepare a text file containing up to 253 tokens, one token per line, and pass it to the fuzzer as -tokens=TOKENS_FILE.txt. Three implicit tokens are added: " ", "\t", and "\n". The fuzzer itself will still be mutating a string of bytes but before passing this input to the target library it will replace every byte b with the b-th token. If there are less than b tokens, a space will be added instead.

AFL compatibility

LibFuzzer can be used in parallel with AFL on the same test corpus. Both fuzzers expect the test corpus to reside in a directory, one file per input. You can run both fuzzers on the same corpus in parallel:

./afl-fuzz -i testcase_dir -o findings_dir /path/to/program -r @@
./llvm-fuzz testcase_dir findings_dir  # Will write new tests to testcase_dir

Periodically restart both fuzzers so that they can use each other's findings.

How good is my fuzzer?

Once you implement your target function LLVMFuzzerTestOneInput and fuzz it to death, you will want to know whether the function or the corpus can be improved further. One easy to use metric is, of course, code coverage. You can get the coverage for your corpus like this:

ASAN_OPTIONS=coverage_pcs=1 ./fuzzer CORPUS_DIR -runs=0

This will run all the tests in the CORPUS_DIR but will not generate any new tests and dump covered PCs to disk before exiting. Then you can subtract the set of covered PCs from the set of all instrumented PCs in the binary, see SanitizerCoverage for details.

User-supplied mutators

LibFuzzer allows to use custom (user-supplied) mutators, see FuzzerInterface.h

Fuzzing components of LLVM

clang-format-fuzzer

The inputs are random pieces of C++-like text.

Build (make sure to use fresh clang as the host compiler):

cmake -GNinja  -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DLLVM_USE_SANITIZER=Address -DLLVM_USE_SANITIZE_COVERAGE=YES -DCMAKE_BUILD_TYPE=Release /path/to/llvm
ninja clang-format-fuzzer
mkdir CORPUS_DIR
./bin/clang-format-fuzzer CORPUS_DIR

Optionally build other kinds of binaries (asan+Debug, msan, ubsan, etc).

TODO: commit the pre-fuzzed corpus to svn (?).

Tracking bug: https://llvm.org/bugs/show_bug.cgi?id=23052

clang-fuzzer

The default behavior is very similar to clang-format-fuzzer. Clang can also be fuzzed with Tokens using -tokens=$LLVM/lib/Fuzzer/cxx_fuzzer_tokens.txt option.

Tracking bug: https://llvm.org/bugs/show_bug.cgi?id=23057

Buildbot

We have a buildbot that runs the above fuzzers for LLVM components 24/7/365 at http://lab.llvm.org:8011/builders/sanitizer-x86_64-linux-fuzzer .

Pre-fuzzed test inputs in git

The buildbot occumulates large test corpuses over time. The corpuses are stored in git on github and can be used like this:

git clone https://github.com/kcc/fuzzing-with-sanitizers.git
bin/clang-format-fuzzer fuzzing-with-sanitizers/llvm/clang-format/C1
bin/clang-fuzzer        fuzzing-with-sanitizers/llvm/clang/C1/
bin/clang-fuzzer        fuzzing-with-sanitizers/llvm/clang/TOK1  -tokens=$LLVM/llvm/lib/Fuzzer/cxx_fuzzer_tokens.txt

FAQ

Q. Why Fuzzer does not use any of the LLVM support?

There are two reasons.

First, we want this library to be used outside of the LLVM w/o users having to build the rest of LLVM. This may sound unconvincing for many LLVM folks, but in practice the need for building the whole LLVM frightens many potential users -- and we want more users to use this code.

Second, there is a subtle technical reason not to rely on the rest of LLVM, or any other large body of code (maybe not even STL). When coverage instrumentation is enabled, it will also instrument the LLVM support code which will blow up the coverage set of the process (since the fuzzer is in-process). In other words, by using more external dependencies we will slow down the fuzzer while the main reason for it to exist is extreme speed.

Q. What about Windows then? The Fuzzer contains code that does not build on Windows.

The sanitizer coverage support does not work on Windows either as of 01/2015. Once it's there, we'll need to re-implement OS-specific parts (I/O, signals).

Q. When this Fuzzer is not a good solution for a problem?

  • If the test inputs are validated by the target library and the validator asserts/crashes on invalid inputs, the in-process fuzzer is not applicable (we could use fork() w/o exec, but it comes with extra overhead).
  • Bugs in the target library may accumulate w/o being detected. E.g. a memory corruption that goes undetected at first and then leads to a crash while testing another input. This is why it is highly recommended to run this in-process fuzzer with all sanitizers to detect most bugs on the spot.
  • It is harder to protect the in-process fuzzer from excessive memory consumption and infinite loops in the target library (still possible).
  • The target library should not have significant global state that is not reset between the runs.
  • Many interesting target libs are not designed in a way that supports the in-process fuzzer interface (e.g. require a file path instead of a byte array).
  • If a single test run takes a considerable fraction of a second (or more) the speed benefit from the in-process fuzzer is negligible.
  • If the target library runs persistent threads (that outlive execution of one test) the fuzzing results will be unreliable.

Q. So, what exactly this Fuzzer is good for?

This Fuzzer might be a good choice for testing libraries that have relatively small inputs, each input takes < 1ms to run, and the library code is not expected to crash on invalid inputs. Examples: regular expression matchers, text or binary format parsers.