atheris

name	atheris
type	fuzzer
description	Sets up and runs Atheris, the coverage-guided Python fuzzer built on libFuzzer. Covers TestOneInput harnesses, FuzzedDataProvider, instrumenting both pure Python and native C extensions, and running under AddressSanitizer. Use when fuzzing a Python package, hunting memory corruption in a Python C extension, or choosing between Atheris and Hypothesis for a Python target.

Atheris

Atheris is a coverage-guided Python fuzzer built on libFuzzer. It enables fuzzing of both pure Python code and Python C extensions with integrated AddressSanitizer support for detecting memory corruption issues.

When to Use

Fuzzer	Best For	Complexity
Atheris	Python code and C extensions	Low-Medium
Hypothesis	Property-based testing	Low
python-afl	AFL-style fuzzing	Medium

Choose Atheris when:

• Fuzzing pure Python code with coverage guidance
• Testing Python C extensions for memory corruption
• Integration with libFuzzer ecosystem is desired
• AddressSanitizer support is needed

Quick Start

import sys
import atheris

@atheris.instrument_func
def TestOneInput(data: bytes):
    if len(data) == 4:
        if data[0] == 0x46:  # "F"
            if data[1] == 0x55:  # "U"
                if data[2] == 0x5A:  # "Z"
                    if data[3] == 0x5A:  # "Z"
                        raise RuntimeError("You caught me")

def main():
    atheris.Setup(sys.argv, TestOneInput)
    atheris.Fuzz()

if __name__ == "__main__":
    main()

Run:

uv run python fuzz.py

Installation

Atheris supports 32-bit and 64-bit Linux, and macOS. We recommend fuzzing on Linux because it's simpler to manage and often faster.

Prerequisites

• Python 3.7 or later
• Recent version of clang (preferably latest release)
• For Docker users: Docker Desktop

Linux/macOS

uv init --bare   # once, if the harness directory is not yet a uv project
uv add atheris

Docker Environment (Recommended)

For a fully operational Linux environment with all dependencies configured:

# https://hub.docker.com/_/python
ARG PYTHON_VERSION=3.11

FROM python:$PYTHON_VERSION-slim-bookworm

RUN python --version

RUN apt update && apt install -y \
    ca-certificates \
    wget \
    && rm -rf /var/lib/apt/lists/*

# LLVM builds version 15-19 for Debian 12 (Bookworm)
# https://apt.llvm.org/bookworm/dists/
ARG LLVM_VERSION=19

RUN echo "deb http://apt.llvm.org/bookworm/ llvm-toolchain-bookworm-$LLVM_VERSION main" > /etc/apt/sources.list.d/llvm.list
RUN echo "deb-src http://apt.llvm.org/bookworm/ llvm-toolchain-bookworm-$LLVM_VERSION main" >> /etc/apt/sources.list.d/llvm.list
RUN wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key > /etc/apt/trusted.gpg.d/apt.llvm.org.asc

RUN apt update && apt install -y \
    build-essential \
    clang-$LLVM_VERSION \
    && rm -rf /var/lib/apt/lists/*

ENV APP_DIR "/app"
RUN mkdir $APP_DIR
WORKDIR $APP_DIR

ENV VIRTUAL_ENV "/opt/venv"
RUN python -m venv $VIRTUAL_ENV
ENV PATH "$VIRTUAL_ENV/bin:$PATH"

# https://github.com/google/atheris/blob/master/native_extension_fuzzing.md#step-1-compiling-your-extension
ENV CC="clang-$LLVM_VERSION"
ENV CFLAGS "-fsanitize=address,fuzzer-no-link"
ENV CXX="clang++-$LLVM_VERSION"
ENV CXXFLAGS "-fsanitize=address,fuzzer-no-link"
ENV LDSHARED="clang-$LLVM_VERSION -shared"
ENV LDSHAREDXX="clang++-$LLVM_VERSION -shared"
ENV ASAN_SYMBOLIZER_PATH="/usr/bin/llvm-symbolizer-$LLVM_VERSION"

# Allow Atheris to find fuzzer sanitizer shared libs
# https://github.com/google/atheris#building-from-source
RUN LIBFUZZER_LIB=$($CC -print-file-name=libclang_rt.fuzzer_no_main-$(uname -m).a) \
    python -m pip install --no-binary atheris atheris

# https://github.com/google/atheris/blob/master/native_extension_fuzzing.md#option-a-sanitizerlibfuzzer-preloads
ENV LD_PRELOAD "$VIRTUAL_ENV/lib/python3.11/site-packages/asan_with_fuzzer.so"

# 1. Skip memory allocation failures for now, they are common, and low impact (DoS)
# 2. https://github.com/google/atheris/blob/master/native_extension_fuzzing.md#leak-detection
ENV ASAN_OPTIONS "allocator_may_return_null=1,detect_leaks=0"

CMD ["/bin/bash"]

Build and run:

docker build -t atheris .
docker run -it atheris

Verification

python -c "import atheris; print(atheris.__version__)"

Writing a Harness

Harness Structure for Pure Python

import sys
import atheris

@atheris.instrument_func
def TestOneInput(data: bytes):
    """
    Fuzzing entry point. Called with random byte sequences.

    Args:
        data: Random bytes generated by the fuzzer
    """
    # Add input validation if needed
    if len(data) < 1:
        return

    # Call your target function
    try:
        your_target_function(data)
    except ValueError:
        # Expected exceptions should be caught
        pass
    # Let unexpected exceptions crash (that's what we're looking for!)

def main():
    atheris.Setup(sys.argv, TestOneInput)
    atheris.Fuzz()

if __name__ == "__main__":
    main()

Structured Input with FuzzedDataProvider

A target taking several typed arguments wastes most of the fuzzer's inputs if the harness slices data by hand, because every mutation shifts the byte offsets of everything after it. atheris.FuzzedDataProvider splits one bytes input into typed values instead:

fdp = atheris.FuzzedDataProvider(data)
name = fdp.ConsumeUnicodeNoSurrogates(fdp.ConsumeIntInRange(0, 64))
strict = fdp.ConsumeBool()

See structured-input.md for the full method reference, the fixed-draw- order rule, and what each method returns once the buffer runs dry.

Harness Rules

Do	Don't
Use @atheris.instrument_func for coverage	Forget to instrument target code
Catch expected exceptions	Catch all exceptions indiscriminately
Use atheris.instrument_imports() for libraries	Import modules after atheris.Setup()
Keep harness deterministic	Use randomness or time-based behavior

See Also: For detailed harness writing techniques, patterns for handling complex inputs, and advanced strategies, see the fuzz-harness-writing technique skill.

Fuzzing Pure Python Code

For fuzzing broader parts of an application or library, use instrumentation functions:

import atheris
with atheris.instrument_imports():
    import your_module
    from another_module import target_function

def TestOneInput(data: bytes):
    target_function(data)

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

Instrumentation Options:

• atheris.instrument_func - Decorator for single function instrumentation
• atheris.instrument_imports() - Context manager for instrumenting all imported modules
• atheris.instrument_all() - Instrument all Python code system-wide

Fuzzing Python C Extensions

Python C extensions require compilation with specific flags for instrumentation and sanitizer support.

Environment Configuration

If using the provided Dockerfile, these are already configured. For local setup:

export CC="clang"
export CFLAGS="-fsanitize=address,fuzzer-no-link"
export CXX="clang++"
export CXXFLAGS="-fsanitize=address,fuzzer-no-link"
export LDSHARED="clang -shared"

Example: Fuzzing cbor2

Install the extension from source:

CBOR2_BUILD_C_EXTENSION=1 uv add --no-binary-package cbor2 'cbor2==5.6.4'

The --no-binary-package flag ensures the C extension is compiled locally with instrumentation rather than pulled as a prebuilt wheel. Persist that choice with no-binary-package = ["cbor2"] under [tool.uv] in pyproject.toml, or a later uv sync can silently swap in an uninstrumented wheel.

Create cbor2-fuzz.py:

import sys
import atheris

# _cbor2 ensures the C library is imported
from _cbor2 import loads

def TestOneInput(data: bytes):
    try:
        loads(data)
    except Exception:
        # We're searching for memory corruption, not Python exceptions
        pass

def main():
    atheris.Setup(sys.argv, TestOneInput)
    atheris.Fuzz()

if __name__ == "__main__":
    main()

Run:

uv run python cbor2-fuzz.py

Important: When running locally (not in Docker), you must set LD_PRELOAD manually.

Corpus Management

Creating Initial Corpus

mkdir corpus
# Add seed inputs
echo "test data" > corpus/seed1
echo '{"key": "value"}' > corpus/seed2

Run with corpus:

uv run python fuzz.py corpus/

Corpus Minimization

Atheris inherits corpus minimization from libFuzzer:

uv run python fuzz.py -merge=1 new_corpus/ old_corpus/

See Also: For corpus creation strategies, dictionaries, and seed selection, see the fuzzing-corpus technique skill.

Running Campaigns

Basic Run

uv run python fuzz.py

With Corpus Directory

uv run python fuzz.py corpus/

Common Options

# Run for 10 minutes
uv run python fuzz.py -max_total_time=600

# Limit input size
uv run python fuzz.py -max_len=1024

# Run with multiple workers
uv run python fuzz.py -workers=4 -jobs=4

Interpreting Output

Output	Meaning
NEW cov: X	Found new coverage, corpus expanded
pulse cov: X	Periodic status update
exec/s: X	Executions per second (throughput)
corp: X/Yb	Corpus size: X inputs, Y bytes total
ERROR: libFuzzer	Crash detected

Sanitizer Integration

AddressSanitizer (ASan)

AddressSanitizer is automatically integrated when using the provided Docker environment or when compiling with appropriate flags.

For local setup:

export CFLAGS="-fsanitize=address,fuzzer-no-link"
export CXXFLAGS="-fsanitize=address,fuzzer-no-link"

Configure ASan behavior:

export ASAN_OPTIONS="allocator_may_return_null=1,detect_leaks=0"

LD_PRELOAD Configuration

For native extension fuzzing:

export LD_PRELOAD="$(python -c 'import atheris; import os; print(os.path.join(os.path.dirname(atheris.__file__), "asan_with_fuzzer.so"))')"

See Also: For detailed sanitizer configuration, common issues, and advanced flags, see the address-sanitizer and undefined-behavior-sanitizer technique skills.

Common Sanitizer Issues

Issue	Solution
LD_PRELOAD not set	Export LD_PRELOAD to point to asan_with_fuzzer.so
Memory allocation failures	Set ASAN_OPTIONS=allocator_may_return_null=1
Leak detection noise	Set ASAN_OPTIONS=detect_leaks=0
Missing symbolizer	Set ASAN_SYMBOLIZER_PATH to llvm-symbolizer

Advanced Usage

Tips and Tricks

Tip	Why It Helps
Use atheris.instrument_imports() early	Ensures all imports are instrumented for coverage
Start with small max_len	Faster initial fuzzing, gradually increase
Use dictionaries for structured formats	Helps fuzzer understand format tokens
Run multiple parallel instances	Better coverage exploration

Custom Instrumentation

Fine-tune what gets instrumented:

import atheris

# Instrument only specific modules
with atheris.instrument_imports():
    import target_module
# Don't instrument test harness code

def TestOneInput(data: bytes):
    target_module.parse(data)

Performance Tuning

Setting	Impact
-max_len=N	Smaller values = faster execution
-workers=N -jobs=N	Parallel fuzzing for faster coverage
ASAN_OPTIONS=fast_unwind_on_malloc=0	Better stack traces, slower execution

UndefinedBehaviorSanitizer (UBSan)

Add UBSan to catch additional bugs:

export CFLAGS="-fsanitize=address,undefined,fuzzer-no-link"
export CXXFLAGS="-fsanitize=address,undefined,fuzzer-no-link"

Note: Modify flags in Dockerfile if using containerized setup.

Real-World Examples

Two complete harnesses — a pure-Python parser and an HTTP response parser — are in examples.md.

Troubleshooting

Problem	Cause	Solution
No coverage increase	Poor seed corpus or target not instrumented	Add better seeds, verify instrument_imports()
Slow execution	ASan overhead or large inputs	Reduce max_len, use ASAN_OPTIONS=fast_unwind_on_malloc=1
Import errors	Modules imported before instrumentation	Move imports inside instrument_imports() context
Segfault without ASan output	Missing LD_PRELOAD	Set LD_PRELOAD to asan_with_fuzzer.so path
Build failures	Wrong compiler or missing flags	Verify CC, CFLAGS, and clang version

Related Skills

Technique Skills

Skill	Use Case
fuzz-harness-writing	Detailed guidance on writing effective harnesses
address-sanitizer	Memory error detection during fuzzing
undefined-behavior-sanitizer	Catching undefined behavior in C extensions
coverage-analysis	Measuring and improving code coverage
fuzzing-corpus	Building and managing seed corpora

Related Fuzzers

Skill	When to Consider
hypothesis	Property-based testing with type-aware generation
python-afl	AFL-style fuzzing for Python when Atheris isn't available

Resources

Key External Resources

Atheris GitHub Repository Official repository with installation instructions, examples, and documentation for fuzzing both pure Python and native extensions.

Native Extension Fuzzing Guide Comprehensive guide covering compilation flags, LD_PRELOAD setup, sanitizer configuration, and troubleshooting for Python C extensions.

Continuously Fuzzing Python C Extensions Trail of Bits blog post covering CI/CD integration, ClusterFuzzLite setup, and real-world examples of fuzzing Python C extensions in continuous integration pipelines.

ClusterFuzzLite Python Integration Guide for integrating Atheris fuzzing into CI/CD pipelines using ClusterFuzzLite for automated continuous fuzzing.

Video Resources

Videos and tutorials are available in the main Atheris documentation and libFuzzer resources.