Fuzzing Python

Python Fuzzing Skill

Setting up fuzzing for a Python project

Fuzz testing for Python projects are Atheris. Atheris is a coverage-guided Python fuzzing engine. It supports fuzzing of Python code, but also native extensions written for CPython. Atheris is based off of libFuzzer. When fuzzing native code, Atheris can be used in combination with Address Sanitizer or Undefined Behavior Sanitizer to catch extra bugs.

You can install prebuilt versions of Atheris with pip:

pip3 install atheris

These wheels come with a built-in libFuzzer, which is fine for fuzzing Python code. If you plan to fuzz native extensions, you may need to build from source to ensure the libFuzzer version in Atheris matches your Clang version.

Using Atheris

Example

#!/usr/bin/python3

import atheris

with atheris.instrument_imports():
  import some_library
  import sys

def TestOneInput(data):
  some_library.parse(data)

atheris.Setup(sys.argv, TestOneInput)
atheris.Fuzz()

When fuzzing Python, Atheris will report a failure if the Python code under test throws an uncaught exception.

Python coverage

Atheris collects Python coverage information by instrumenting bytecode. There are 3 options for adding this instrumentation to the bytecode:

• You can instrument the libraries you import:

  with atheris.instrument_imports():
    import foo
    from bar import baz
  

  This will cause instrumentation to be added to foo and bar, as well as any libraries they import.

• Or, you can instrument individual functions:

  @atheris.instrument_func
  def my_function(foo, bar):
    print("instrumented")
  

• Or finally, you can instrument everything:

  atheris.instrument_all()
  

  Put this right before atheris.Setup(). This will find every Python function currently loaded in the interpreter, and instrument it. This might take a while.

Atheris can additionally instrument regular expression checks, e.g. re.search. To enable this feature, you will need to add: atheris.enabled_hooks.add("RegEx") To your script before your code calls re.compile. Internally this will import the re module and instrument the necessary functions. This is currently an experimental feature.

Similarly, Atheris can instrument str methods; currently only str.startswith and str.endswith are supported. To enable this feature, add atheris.enabled_hooks.add("str"). This is currently an experimental feature.

Why am I getting "No interesting inputs were found"?

You might see this error:

ERROR: no interesting inputs were found. Is the code instrumented for coverage? Exiting.

You'll get this error if the first 2 calls to TestOneInput didn't produce any coverage events. Even if you have instrumented some Python code, this can happen if the instrumentation isn't reached in those first 2 calls. (For example, because you have a nontrivial TestOneInput). You can resolve this by adding an atheris.instrument_func decorator to TestOneInput, using atheris.instrument_all(), or moving your TestOneInput function into an instrumented module.

Visualizing Python code coverage

Examining which lines are executed is helpful for understanding the effectiveness of your fuzzer. Atheris is compatible with coverage.py: you can run your fuzzer using the coverage.py module as you would for any other Python program. Here's an example:

python3 -m coverage run your_fuzzer.py -atheris_runs=10000  # Times to run
python3 -m coverage html
(cd htmlcov && python3 -m http.server 8000)

Coverage reports are only generated when your fuzzer exits gracefully. This happens if:

• you specify -atheris_runs=<number>, and that many runs have elapsed.
• your fuzzer exits by Python exception.
• your fuzzer exits by sys.exit().

No coverage report will be generated if your fuzzer exits due to a crash in native code, or due to libFuzzer's -runs flag (use -atheris_runs). If your fuzzer exits via other methods, such as SIGINT (Ctrl+C), Atheris will attempt to generate a report but may be unable to (depending on your code). For consistent reports, we recommend always using -atheris_runs=<number>.

If you'd like to examine coverage when running with your corpus, you can do that with the following command:

python3 -m coverage run your_fuzzer.py corpus_dir/* -atheris_runs=$(( 1 + $(ls corpus_dir | wc -l) ))

This will cause Atheris to run on each file in <corpus-dir>, then exit. Note: atheris use empty data set as the first input even if there is no empty file in <corpus_dir>. Importantly, if you leave off the -atheris_runs=$(ls corpus_dir | wc -l), no coverage report will be generated.

Using coverage.py will significantly slow down your fuzzer, so only use it for visualizing coverage; don't use it all the time.

Fuzzing Native Extensions

In order for fuzzing native extensions to be effective, your native extensions must be instrumented. See Native Extension Fuzzing for instructions.

Structure-aware Fuzzing

Atheris is based on a coverage-guided mutation-based fuzzer (LibFuzzer). This has the advantage of not requiring any grammar definition for generating inputs, making its setup easier. The disadvantage is that it will be harder for the fuzzer to generate inputs for code that parses complex data types. Often the inputs will be rejected early, resulting in low coverage.

Atheris supports custom mutators (as offered by LibFuzzer) to produce grammar-aware inputs.

Example (Atheris-equivalent of the example in the LibFuzzer docs):

@atheris.instrument_func
def TestOneInput(data):
  try:
    decompressed = zlib.decompress(data)
  except zlib.error:
    return

  if len(decompressed) < 2:
    return

  try:
    if decompressed.decode() == 'FU':
      raise RuntimeError('Boom')
  except UnicodeDecodeError:
    pass

To reach the RuntimeError crash, the fuzzer needs to be able to produce inputs that are valid compressed data and satisfy the checks after decompression. It is very unlikely that Atheris will be able to produce such inputs: mutations on the input data will most probably result in invalid data that will fail at decompression-time.

To overcome this issue, you can define a custom mutator function (equivalent to LLVMFuzzerCustomMutator). This example produces valid compressed data. To enable Atheris to make use of it, pass the custom mutator function to the invocation of atheris.Setup.

def CustomMutator(data, max_size, seed):
  try:
    decompressed = zlib.decompress(data)
  except zlib.error:
    decompressed = b'Hi'
  else:
    decompressed = atheris.Mutate(decompressed, len(decompressed))
  return zlib.compress(decompressed)

atheris.Setup(sys.argv, TestOneInput, custom_mutator=CustomMutator)
atheris.Fuzz()

As seen in the example, the custom mutator may request Atheris to mutate data using atheris.Mutate() (this is equivalent to LLVMFuzzerMutate).

You can experiment with custom_mutator_example.py and see that without the mutator Atheris would not be able to find the crash, while with the mutator this is achieved in a matter of seconds.

$ python3 example_fuzzers/custom_mutator_example.py --no_mutator
[...]
#2      INITED cov: 2 ft: 2 corp: 1/1b exec/s: 0 rss: 37Mb
#524288 pulse  cov: 2 ft: 2 corp: 1/1b lim: 4096 exec/s: 262144 rss: 37Mb
#1048576        pulse  cov: 2 ft: 2 corp: 1/1b lim: 4096 exec/s: 349525 rss: 37Mb
#2097152        pulse  cov: 2 ft: 2 corp: 1/1b lim: 4096 exec/s: 299593 rss: 37Mb
#4194304        pulse  cov: 2 ft: 2 corp: 1/1b lim: 4096 exec/s: 279620 rss: 37Mb
[...]

$ python3 example_fuzzers/custom_mutator_example.py
[...]
INFO: found LLVMFuzzerCustomMutator (0x7f9c989fb0d0). Disabling -len_control by default.
[...]
#2      INITED cov: 2 ft: 2 corp: 1/1b exec/s: 0 rss: 37Mb
#3      NEW    cov: 4 ft: 4 corp: 2/11b lim: 4096 exec/s: 0 rss: 37Mb L: 10/10 MS: 1 Custom-
#12     NEW    cov: 5 ft: 5 corp: 3/21b lim: 4096 exec/s: 0 rss: 37Mb L: 10/10 MS: 7 Custom-CrossOver-Custom-CrossOver-Custom-ChangeBit-Custom-
 === Uncaught Python exception: ===
RuntimeError: Boom
Traceback (most recent call last):
  File "example_fuzzers/custom_mutator_example.py", line 62, in TestOneInput
    raise RuntimeError('Boom')
[...]

Custom crossover functions (equivalent to LLVMFuzzerCustomCrossOver) are also supported. You can pass the custom crossover function to the invocation of atheris.Setup. See its usage in custom_crossover_fuzz_test.py.

Structure-aware Fuzzing with Protocol Buffers

libprotobuf-mutator has bindings to use it together with Atheris to perform structure-aware fuzzing using protocol buffers.

See the documentation for atheris_libprotobuf_mutator.

Integration with OSS-Fuzz

Atheris is fully supported by OSS-Fuzz, Google's continuous fuzzing service for open source projects. For integrating with OSS-Fuzz, please see https://google.github.io/oss-fuzz/getting-started/new-project-guide/python-lang.

API

The atheris module provides three key functions: instrument_imports(), Setup() and Fuzz().

In your source file, import all libraries you wish to fuzz inside a with atheris.instrument_imports():-block, like this:

# library_a will not get instrumented
import library_a

with atheris.instrument_imports():
    # library_b will get instrumented
    import library_b

Generally, it's best to import atheris first and then import all other libraries inside of a with atheris.instrument_imports() block.

Next, define a fuzzer entry point function and pass it to atheris.Setup() along with the fuzzer's arguments (typically sys.argv). Finally, call atheris.Fuzz() to start fuzzing. You must call atheris.Setup() before atheris.Fuzz().

instrument_imports(include=[], exclude=[])

• include: A list of fully-qualified module names that shall be instrumented.
• exclude: A list of fully-qualified module names that shall NOT be instrumented.

This should be used together with a with-statement. All modules imported in said statement will be instrumented. However, because Python imports all modules only once, this cannot be used to instrument any previously imported module, including modules required by Atheris. To add coverage to those modules, use instrument_all() instead.

A full list of unsupported modules can be retrieved as follows:

import sys
import atheris
print(sys.modules.keys())

instrument_func(func)

• func: The function to instrument.

This will instrument the specified Python function and then return func. This is typically used as a decorator, but can be used to instrument individual functions too. Note that the func is instrumented in-place, so this will affect all call points of the function.

This cannot be called on a bound method - call it on the unbound version.

instrument_all()

This will scan over all objects in the interpreter and call instrument_func on every Python function. This works even on core Python interpreter functions, something which instrument_imports cannot do.

This function is experimental.

Setup(args, test_one_input, internal_libfuzzer=None)

• args: A list of strings: the process arguments to pass to the fuzzer, typically sys.argv. This argument list may be modified in-place, to remove arguments consumed by the fuzzer. See the LibFuzzer docs for a list of such options.
• test_one_input: your fuzzer's entry point. Must take a single bytes argument. This will be repeatedly invoked with a single bytes container.
• internal_libfuzzer: Indicates whether libfuzzer will be provided by atheris or by an external library (see native_extension_fuzzing.md). If unspecified, Atheris will determine this automatically. If fuzzing pure Python, leave this as True.

Fuzz()

This starts the fuzzer. You must have called Setup() before calling this function. This function does not return.

In many cases Setup() and Fuzz() could be combined into a single function, but they are separated because you may want the fuzzer to consume the command-line arguments it handles before passing any remaining arguments to another setup function.

FuzzedDataProvider

Often, a bytes object is not convenient input to your code being fuzzed. Similar to libFuzzer, we provide a FuzzedDataProvider to translate these bytes into other input forms.

You can construct the FuzzedDataProvider with:

fdp = atheris.FuzzedDataProvider(input_bytes)

The FuzzedDataProvider then supports the following functions:

def ConsumeBytes(count: int)

Consume count bytes.

def ConsumeUnicode(count: int)

Consume unicode characters. Might contain surrogate pair characters, which according to the specification are invalid in this situation. However, many core software tools (e.g. Windows file paths) support them, so other software often needs to too.

def ConsumeUnicodeNoSurrogates(count: int)

Consume unicode characters, but never generate surrogate pair characters.

def ConsumeString(count: int)

Alias for ConsumeBytes in Python 2, or ConsumeUnicode in Python 3.

def ConsumeInt(int: bytes)

Consume a signed integer of the specified size (when written in two's complement notation).

def ConsumeUInt(int: bytes)

Consume an unsigned integer of the specified size.

def ConsumeIntInRange(min: int, max: int)

Consume an integer in the range [min, max].

def ConsumeIntList(count: int, bytes: int)

Consume a list of count integers of size bytes.

def ConsumeIntListInRange(count: int, min: int, max: int)

Consume a list of count integers in the range [min, max].

def ConsumeFloat()

Consume an arbitrary floating-point value. Might produce weird values like NaN and Inf.

def ConsumeRegularFloat()

Consume an arbitrary numeric floating-point value; never produces a special type like NaN or Inf.

def ConsumeProbability()

Consume a floating-point value in the range [0, 1].

def ConsumeFloatInRange(min: float, max: float)

Consume a floating-point value in the range [min, max].

def ConsumeFloatList(count: int)

Consume a list of count arbitrary floating-point values. Might produce weird values like NaN and Inf.

def ConsumeRegularFloatList(count: int)

Consume a list of count arbitrary numeric floating-point values; never produces special types like NaN or Inf.

def ConsumeProbabilityList(count: int)

Consume a list of count floats in the range [0, 1].

def ConsumeFloatListInRange(count: int, min: float, max: float)

Consume a list of count floats in the range [min, max]

def PickValueInList(l: list)

Given a list, pick a random value

def ConsumeBool()

Consume either True or False.

Important considerations for fuzz targets

Some important things to remember about fuzz targets:

• The fuzzing engine will execute the fuzz target many times with different inputs in the same process.
• It must tolerate any kind of input (empty, huge, malformed, etc).
• It must not exit() on any input.
• It may use threads but ideally all threads should be joined at the end of the function.
• It must be as deterministic as possible. Non-determinism (e.g. random decisions not based on the input bytes) will make fuzzing inefficient.
• It must be fast. Try avoiding cubic or greater complexity, logging, or excessive memory consumption.
• Ideally, it should not modify any global state (although that’s not strict).
• Usually, the narrower the target the better. E.g. if your target can parse several data formats, split it into several targets, one per format.