llvm/docs/LibFuzzer.rst

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LibFuzzer -- a library for coverage-guided fuzz testing.
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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=[234]
  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 TestOneInput(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.
* 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. For run-time options run the Fuzzer binary with '-help=1'.


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.

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 TestOneInput(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=3 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=4 -mllvm -sanitizer-coverage-8bit-counters=1"
  # 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 the actual function that does something interesting with PCRE2.
  cat << EOF > pcre_fuzzer.cc
  #include <string.h>
  #include "pcre2posix.h"
  extern "C" void TestOneInput(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)

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

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

This is useful when you already have an exhaustive test corpus.
If you've just started fuzzing with no good corpus running independent
jobs will create a corpus with too many duplicates.
One way to avoid this and still use all of your CPUs is to use the flag ``-exit_on_first=1``
which will cause the fuzzer to exit on the first new synthesised input::

  N=100; M=4; ./pcre_fuzzer ./CORPUS -jobs=$N -workers=$M -exit_on_first=1

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.


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

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.

.. _pcre2: http://www.pcre.org/

.. _AFL: http://lcamtuf.coredump.cx/afl/

.. _SanitizerCoverage: https://code.google.com/p/address-sanitizer/wiki/AsanCoverage