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https://github.com/d0c-s4vage/gramfuzz

gramfuzz is a grammar-based fuzzer that lets one define complex grammars to generate text and binary data formats.
https://github.com/d0c-s4vage/gramfuzz

complex-grammars fuzzer fuzzing grammar parsing

Last synced: 12 days ago
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gramfuzz is a grammar-based fuzzer that lets one define complex grammars to generate text and binary data formats.

Host: GitHub
URL: https://github.com/d0c-s4vage/gramfuzz
Owner: d0c-s4vage
License: mit
Created: 2016-12-12T04:51:52.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2020-10-02T08:04:16.000Z (about 4 years ago)
Last Synced: 2024-10-15T09:54:51.083Z (28 days ago)
Topics: complex-grammars, fuzzer, fuzzing, grammar, parsing
Language: Python
Homepage: https://d0c-s4vage.github.io/gramfuzz/
Size: 6.19 MB
Stars: 253
Watchers: 18
Forks: 39
Open Issues: 9
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md

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README

        # gramfuzz

[![Master Build Status](https://travis-ci.org/d0c-s4vage/gramfuzz.svg?branch=master)](https://travis-ci.org/d0c-s4vage/gramfuzz)

[![PyPI Statistics](https://img.shields.io/pypi/dm/gramfuzz)](https://pypistats.org/packages/gramfuzz)

[![Latest Release](https://img.shields.io/pypi/v/gramfuzz)](https://pypi.python.org/pypi/gramfuzz/)

`gramfuzz` is a grammar-based fuzzer that lets one define

complex grammars to model text and binary data formats.

- [Contributing](#contributing)

- [Installation](#installation)

- [Documentation](#documentation)

- [TLDR Example](#tldr-example)

- [More Examples](#more-examples)

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md) for details. PRs welcome!

## Installation

Install via pip:

```

pip install gramfuzz

```

## Documentation

For detailed documentation, please [view the full docs here](https://d0c-s4vage.github.io/gramfuzz/)

## TLDR Example

Suppose we define a grammar for generating names and their prefixes

and suffixes:

```python

# names_grammar.py

import gramfuzz

from gramfuzz.fields import *

class NRef(Ref):

    cat = "name_def"

class NDef(Def):

    cat = "name_def"

Def("name",

    Opt(NRef("name_title")),

    NRef("personal_part"),

    NRef("last_name"),

    Opt(NRef("name_suffix")),

cat="name", sep=" ")

NDef("personal_part",

    NRef("initial") | NRef("first_name"), Opt(NRef("personal_part")),

sep=" ")

NDef("last_name", Or(

    "Blart", "Tralb"

))

NDef("name_suffix",

    Opt(NRef("seniority")),

    Or("Phd.", "CISSP", "MD.", "MBA", "NBA", "NFL", "WTF", "The Great"),

sep=" ")

NDef("seniority", Or("Sr.", "Jr."))

NDef("name_title", Or(

    "Sir", "Ms.", "Mr.", "Mrs.", "Senator", "Capt.", "Maj.", "Lt.", "President"

))

NDef("first_name", Or("Henry", "Susy"))

NDef("initial",

    String(min=1, max=2, charset=String.charset_alpha_upper), "."

)

```

We could then use this grammar like so:

```python

import gramfuzz

fuzzer = gramfuzz.GramFuzzer()

fuzzer.load_grammar("names_grammar.py")

names = fuzzer.gen(cat="name", num=10)

print("\n".join(names))

```

Which would output something like this:

```

Ms. Susy Henry Tralb

Lt. Henry Henry Tralb

L. Tralb WTF

Maj. L. W. N. Tralb

Z. Tralb

Senator C. K. Henry Blart

Henry Tralb CISSP

Lt. Henry Tralb Jr. NBA

Maj. Susy Tralb Sr. NBA

Henry C. Blart WTF

```

## More Examples

See the examples (and example script) in the examples folder:

```

 lptp [ tmp ]: git clone https://github.com/d0c-s4vage/gramfuzz

 lptp [ tmp ]: cd gramfuzz/examples

 lptp [ examples ]: ./example.py --help

usage: gramfuzz/examples/example.py [-h] -g GRAMMAR [-n N] [-s RAND_SEED]

                                    [--max-recursion R] [-o OUTPUT]

This script will generate N instances of the specified grammar.

optional arguments:

  -h, --help            show this help message and exit

  -g GRAMMAR, --grammar GRAMMAR

                        The grammar to load. One of: names,python27,roman_numeral,postal

  -n N, --number N      The number of times to generate top-level nodes from the specified grammar(s) (default=1)

  -s RAND_SEED, --seed RAND_SEED

                        The random seed to initialize the PRNG with (default=None)

  --max-recursion R     The maximum reference recursion depth allowed (default=10)

  -o OUTPUT, --output OUTPUT

                        The output file to output the generated data to (default=stdout)

 lptp [ examples ]: ./example.py -g postal -n 10 -s 1337 --max-recursion 5

Z. Tralb

69 Baker Street 8325U 

Malang, IL 64666-4973

Senator Susy Henry Blart WTF

56 Sesame Street 

Yokohama, WV 49471-3667

Henry I. Tralb Jr. CISSP

63 Spooner Street 858H 

Tehran, TX 27259-9556

Capt. Henry Susy Blart Jr. Phd.

65536 Jump Street 

Malang, ID 84108-0969

Susy Blart

0 Rainey Street 

Wuhan, FL 16712-1095

P. Blart NFL

98 Wisteria Lane 

Shenyang, NH 70126

Henry Henry Blart Phd.

30 Rainey Street 

Madras, GA 90915

Senator Henry E. Tralb

38 Spooner Street 

Tianjin, CT 37211

Maj. Henry Tralb

70 Rainey Street 458W 

HongKong, OK 40689

Mrs. Henry Blart

11 Sesame Street 

Beijing, MT 58689-7258

```

## Contributors

* @docfate111