gramfuzz is a grammar-based fuzzer that lets one define
complex grammars to model text and binary data formats.
See CONTRIBUTING.md for details. PRs welcome!
Install via pip:
pip install gramfuzz
For detailed documentation, please view the full docs here
Suppose we define a grammar for generating names and their prefixes and suffixes:
# 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:
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
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