atheris
Atheris is a coverage-guided Python fuzzer based on libFuzzer. Use for fuzzing pure Python code and Python C extensions.
Install
mkdir -p .claude/skills/atheris && curl -L -o skill.zip "https://mcp.directory/api/skills/download/3476" && unzip -o skill.zip -d .claude/skills/atheris && rm skill.zipInstalls to .claude/skills/atheris
About this skill
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 test_one_input(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, test_one_input)
atheris.Fuzz()
if __name__ == "__main__":
main()
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 pip install 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 test_one_input(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, test_one_input)
atheris.Fuzz()
if __name__ == "__main__":
main()
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 test_one_input(data: bytes):
target_function(data)
atheris.Setup(sys.argv, test_one_input)
atheris.Fuzz()
Instrumentation Options:
atheris.instrument_func- Decorator for single function instrumentationatheris.instrument_imports()- Context manager for instrumenting all imported modulesatheris.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 python -m pip install --no-binary cbor2 cbor2==5.6.4
The --no-binary flag ensures the C extension is compiled locally with instrumentation.
Create cbor2-fuzz.py:
import sys
import atheris
# _cbor2 ensures the C library is imported
from _cbor2 import loads
def test_one_input(data: bytes):
try:
loads(data)
except Exception:
# We're searching for memory corruption, not Python exceptions
pass
def main():
atheris.Setup(sys.argv, test_one_input)
atheris.Fuzz()
if __name__ == "__main__":
main()
Run:
python cbor2-fuzz.py
Important: When running locally (not in Docker), you must set
LD_PRELOADmanually.
Corpus Management
Creating Initial Corpus
mkdir corpus
# Add seed inputs
echo "test data" > corpus/seed1
echo '{"key": "value"}' > corpus/seed2
Run with corpus:
python fuzz.py corpus/
Corpus Minimization
Atheris inherits corpus minimization from libFuzzer:
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
python fuzz.py
With Corpus Directory
python fuzz.py corpus/
Common Options
# Run for 10 minutes
python fuzz.py -max_total_time=600
# Limit input size
python fuzz.py -max_len=1024
# Run with multiple workers
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
Content truncated.
More by trailofbits
View all skills by trailofbits →You might also like
flutter-development
aj-geddes
Build beautiful cross-platform mobile apps with Flutter and Dart. Covers widgets, state management with Provider/BLoC, navigation, API integration, and material design.
drawio-diagrams-enhanced
jgtolentino
Create professional draw.io (diagrams.net) diagrams in XML format (.drawio files) with integrated PMP/PMBOK methodologies, extensive visual asset libraries, and industry-standard professional templates. Use this skill when users ask to create flowcharts, swimlane diagrams, cross-functional flowcharts, org charts, network diagrams, UML diagrams, BPMN, project management diagrams (WBS, Gantt, PERT, RACI), risk matrices, stakeholder maps, or any other visual diagram in draw.io format. This skill includes access to custom shape libraries for icons, clipart, and professional symbols.
ui-ux-pro-max
nextlevelbuilder
"UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 8 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient."
godot
bfollington
This skill should be used when working on Godot Engine projects. It provides specialized knowledge of Godot's file formats (.gd, .tscn, .tres), architecture patterns (component-based, signal-driven, resource-based), common pitfalls, validation tools, code templates, and CLI workflows. The `godot` command is available for running the game, validating scripts, importing resources, and exporting builds. Use this skill for tasks involving Godot game development, debugging scene/resource files, implementing game systems, or creating new Godot components.
nano-banana-pro
garg-aayush
Generate and edit images using Google's Nano Banana Pro (Gemini 3 Pro Image) API. Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports both text-to-image generation and image-to-image editing with configurable resolution (1K default, 2K, or 4K for high resolution). DO NOT read the image file first - use this skill directly with the --input-image parameter.
fastapi-templates
wshobson
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
Related MCP Servers
Browse all serversAnalyze Python, Go, and TypeScript code locally to automatically generate IAM policies and AWS IAM permissions for least
Unlock powerful image manipulation with ImageSorcery: resize, crop, detect objects, and perform optical character recogn
Fused MCP Agents — Python-based MCP server to run Python from Claude, enabling Claude Python integration and LLM Python
Explore Sequential Story for advanced problem solving techniques and methodologies using narrative or Python-based struc
Log Analyzer offers advanced Python log analysis, pattern filtering, pytest output parsing, and code coverage reporting
Learn how to use Python to read a file and manipulate local files safely through the Filesystem API.
Stay ahead of the MCP ecosystem
Get weekly updates on new skills and servers.