Agent skill

Fuzzing Python

by benchflow-ai in benchflow-ai/skillsbench

Creating fuzz driver for Python libraries using LibFuzzer. An agent skill from benchflow-ai/skillsbench.

Apache-2.0Auto-check passedSecurity

Install Fuzzing Python

skills CLI
$ npx skills add benchflow-ai/skillsbench --skill fuzzing-python -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install benchflow-ai/skillsbench fuzzing-python --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/setup-fuzzing-py/environment/skills/fuzzing-python .claude/skills/fuzzing-python && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fuzzing-python
GitHub stars
1.8k
Token cost
~4.2k tokens
SKILL.md length
1,732 words
Files
1
Skills in repo
180
Repo updated
First seen
Licence
Apache-2.0

At a glance

Creating fuzz driver for Python libraries using LibFuzzer. An agent skill from benchflow-ai/skillsbench.

  • Tasks that involve Fuzzing
  • SKILL.md covers Setting up fuzzing for a…, Using Atheris, Integration with OSS-Fuzz and API, plus 1 more section
  • Calls python3 and pip3

What it does

Fuzzing Python is an agent skill from benchflow-ai/skillsbench. Creating fuzz driver for Python libraries using LibFuzzer. This skill is useful when agent needs to work with creating fuzz drivers / fuzz targets for Python project and libraries.

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Security, covering Fuzzing. It works with Python. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Fuzzing

Example prompts

  • “/fuzzing-python”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3
    • pip3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com
    • google.github.io
    • coverage.readthedocs.io
    • llvm.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fuzzing Python loads about 4.2k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 1,732 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 1,732 words, ~4,231 tokens.

Download SKILL.mdSave it as .claude/skills/fuzzing-python/SKILL.md (or your agent's skills folder).
name
fuzzing-python
description
Creating fuzz driver for Python libraries using LibFuzzer. This skill is useful when agent needs to work with creating fuzz drivers / fuzz targets for Python project and libraries.
license
Apache License 2.0. https://github.com/google/atheris/blob/775b08fb1a781142540995e8a2817c48ffae343f/LICENSE

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:

sh
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
python
#!/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:

    python
    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:

    python
    @atheris.instrument_func
    def my_function(foo, bar):
      print("instrumented")
  • Or finally, you can instrument everything:

    python
    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:

bash
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):

python
@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.

python
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.

shell
$ 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:

python
# 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().

Show full SKILL.md (736 more words)Show less
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:

python
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:

python
fdp = atheris.FuzzedDataProvider(input_bytes)

The FuzzedDataProvider then supports the following functions:

python
def ConsumeBytes(count: int)

Consume count bytes.

python
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.

python
def ConsumeUnicodeNoSurrogates(count: int)

Consume unicode characters, but never generate surrogate pair characters.

python
def ConsumeString(count: int)

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

python
def ConsumeInt(int: bytes)

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

python
def ConsumeUInt(int: bytes)

Consume an unsigned integer of the specified size.

python
def ConsumeIntInRange(min: int, max: int)

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

python
def ConsumeIntList(count: int, bytes: int)

Consume a list of count integers of size bytes.

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

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

python
def ConsumeFloat()

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

python
def ConsumeRegularFloat()

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

python
def ConsumeProbability()

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

python
def ConsumeFloatInRange(min: float, max: float)

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

python
def ConsumeFloatList(count: int)

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

python
def ConsumeRegularFloatList(count: int)

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

python
def ConsumeProbabilityList(count: int)

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

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

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

python
def PickValueInList(l: list)

Given a list, pick a random value

python
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.

© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in tasks/setup-fuzzing-py/environment/skills/fuzzing-python of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

Fuzzing Python next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Directed Test Input GeneratorArabelaTso/Skills-4-SE253—~3kAutomated safety check: PassApache-2.0
Simplified Python Fuzzeropensage-agent/opensage-adk127—~228Automated safety check: PassApache-2.0
Fuzzing Input GeneratorArabelaTso/Skills-4-SE253—~4.9kAutomated safety check: PassApache-2.0
Security Auditoreigent-ai/eigent15k—~1.8kAutomated safety check: NotesApache-2.0

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Works with

Categories

Questions about Fuzzing Python

What does Fuzzing Python do?

Creating fuzz driver for Python libraries using LibFuzzer. An agent skill from benchflow-ai/skillsbench. Fuzzing Python is an agent skill from benchflow-ai/skillsbench. Creating fuzz driver for Python libraries using LibFuzzer.

When should I use Fuzzing Python?

Fuzzing Python fits situations like: tasks that involve Fuzzing.

How do I install Fuzzing Python in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill fuzzing-python -a claude-code`. Or copy the skill folder (tasks/setup-fuzzing-py/environment/skills/fuzzing-python in benchflow-ai/skillsbench) into .claude/skills/fuzzing-python in your project. Claude Code loads it when a task matches its description.

How do I install Fuzzing Python in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill fuzzing-python -a codex`. Or copy the skill folder (tasks/setup-fuzzing-py/environment/skills/fuzzing-python in benchflow-ai/skillsbench) into .agents/skills/fuzzing-python in your project. Codex loads it when a task matches its description.

Can I use Fuzzing Python in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill fuzzing-python -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fuzzing-python, .gemini/skills/fuzzing-python, .github/skills/fuzzing-python and .opencode/skills/fuzzing-python in your project.

What does Fuzzing Python need to run?

Going by SKILL.md and its folder, Fuzzing Python needs the command-line tools its instructions call (python3 and pip3). Our summary lists: Python 3.

Does Fuzzing Python access the network?

SKILL.md names 4 domains. As links in the text: github.com, google.github.io, coverage.readthedocs.io and llvm.org. This is read from the text; nothing was executed.

Is Fuzzing Python safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fuzzing Python use?

Fuzzing Python is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fuzzing Python use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Fuzzing Python?

Skills that share tags, products or a category with Fuzzing Python: Concurrency Fuzzing Testing (dzhalaevd/Donatello, 135 stars), Directed Test Input Generator (ArabelaTso/Skills-4-SE, 253 stars), Simplified Python Fuzzer (opensage-agent/opensage-adk, 127 stars) and Fuzzing Input Generator (ArabelaTso/Skills-4-SE, 253 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fuzzing Python?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 180 skills in this directory. The repository was last updated on July 23, 2026.

Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.