fuzzing-python-expert

name: fuzzing-python-expert description: Use this skill to fuzz open source Python software projects using Atheris.

Fuzzing Python expert

This skill provides the agent with the knowledge and tools to write, build, and validate fuzz targets for Python projects integrated into OSS-Fuzz. Python fuzzing uses Atheris, which wraps libFuzzer and instruments Python bytecode for coverage-guided fuzzing.

Fundamental Concepts

OSS-Fuzz base image

Python projects must use the Python base builder image:

FROM gcr.io/oss-fuzz-base/base-builder-python

Set language: python in project.yaml.

Harness structure

A Python fuzz target is a .py file (named fuzz_<target>.py by convention) that follows this pattern:

#!/usr/bin/python3
import sys
import atheris

# Import the module under test after atheris.instrument_imports() or within
# the atheris.instrument_all() context so bytecode is instrumented.

def TestOneInput(data):
    fdp = atheris.FuzzedDataProvider(data)
    # Extract typed values from the raw fuzzer bytes.
    value = fdp.ConsumeString(128)
    try:
        my_module.parse(value)
    except (ValueError, TypeError, KeyError):
        # Expected exceptions from invalid input are not bugs.
        pass

def main():
    atheris.instrument_all()   # instrument all loaded Python modules
    atheris.Setup(sys.argv, TestOneInput, enable_python_coverage=True)
    atheris.Fuzz()

if __name__ == "__main__":
    main()

Instrumenting imports selectively

When instrument_all() is too broad (e.g. causes conflicts with C extensions), instrument specific modules using the instrument_imports() context manager:

import atheris
with atheris.instrument_imports():
    import my_module
    import my_module.subpackage

FuzzedDataProvider reference

atheris.FuzzedDataProvider splits the raw byte stream into typed values:

Building in OSS-Fuzz

build.sh installs the target package and uses the compile_python_fuzzer helper to turn each fuzz_*.py file into a standalone fuzzer binary in $OUT:

# build.sh

# Install the package under test.
pip3 install .

# Compile all fuzz targets found in $SRC.
for fuzzer in $(find $SRC -name 'fuzz_*.py'); do
    compile_python_fuzzer "$fuzzer"
done

compile_python_fuzzer handles linking against Atheris and libFuzzer and produces an executable in $OUT named after the .py file.

Seed corpus and dictionaries

• Place seed files in $OUT/<fuzzer_name>_seed_corpus/ or zip them as $OUT/<fuzzer_name>_seed_corpus.zip.
• Dictionaries go to $OUT/<fuzzer_name>.dict — especially valuable for text-format parsers (JSON, XML, YAML, CSV, etc.).
• For targets that parse a structured format, generating seeds with a script beats hand-picking a few files — random mutation rarely passes the parser's early checks. See the structured seed generation reference.

Characteristics of good Python fuzzing harnesses

1. Instruments the right modules: use instrument_all() for simplicity or instrument_imports() for targeted instrumentation. Without instrumentation coverage guidance is blind.
2. Targets attack surface: parsers, template engines, serialisation (pickle, JSON, YAML, XML), network protocol handling, and any API that accepts untrusted strings or bytes.
3. Catches expected exceptions: wrap target calls in try/except for the documented exception types the target raises on bad input. Only unexpected exceptions and hard crashes are findings.
4. Uses FuzzedDataProvider for structured input rather than feeding raw bytes directly to APIs that expect text — most Python APIs work on strings, not bytes.
5. Avoids importing under test inside TestOneInput: imports should happen at module level (inside the instrument_imports() block if used) so they are instrumented and not re-executed per iteration.
6. Is fast: avoid file I/O, network calls, or subprocess invocations inside TestOneInput.
7. Avoids non-determinism: no random, no datetime.now(), no os.urandom() inside the fuzz function.
8. Enables Python coverage: pass enable_python_coverage=True to atheris.Setup for bytecode-level coverage tracking.

What Python fuzzing finds

Python is memory-safe, so the focus is on:

• Unhandled exceptions: ValueError, RecursionError, MemoryError, UnicodeDecodeError, and any exception the library should have caught and converted to a clean error.
• AssertionError: internal invariant violations triggered by crafted input.
• Hangs / infinite loops: detected by OSS-Fuzz's timeout.
• Crashes in C extensions: Python libraries that wrap C code (e.g. lxml, Pillow, cryptography) can still have memory-corruption bugs in their C layer, which Atheris will surface because libFuzzer runs the whole process.
• Logic errors: incorrect output, silent data corruption, wrong parsing results on edge-case inputs.

Operational guidelines

• Always validate with:

  python3 infra/helper.py build_fuzzers <project>
  python3 infra/helper.py check_build <project>
  python3 infra/helper.py run_fuzzer <project> <fuzzer_name> -- -max_total_time=30
  

• An instant crash usually means an exception is being raised on every input. Run the harness locally (python3 fuzz_target.py) on a sample input to debug before building through OSS-Fuzz.
• Test the harness locally before the OSS-Fuzz build:

  python3 fuzz_target.py <seed_file>
  

  Atheris supports running in single-input mode outside libFuzzer.
• Install the package locally (pip3 install .) and iterate quickly before going through the Docker build.
• When iterating locally clone the upstream repo and switch the Dockerfile from RUN git clone to COPY to avoid network round-trips.
• Document why each entry point was chosen and what class of bugs it may find.