CVSSlib 
A Python 3 library for calculating CVSS v2, CVSS v3 and CVSS v3.1 vectors, with tests. Examples on how to use
the library is shown below, and there is some documentation on the internals within the docs directory. The library
is designed to be completely extendable, so it is possible to implement your own custom scoring systems (or those of your clients)
and have it work with the same API, and with the same bells and whistles.
Python 3 only
API
It's pretty simple to use. cvsslib has a cvss2, cvss3 and cvss31 sub modules that contains all of the enums
and calculation code. There are also some functions to manipulate vectors that take these cvss modules
as arguments. E.G:
from cvsslib import cvss2, cvss31, calculate_vector
vector_v2 = "AV:L/AC:M/Au:S/C:N/I:P/A:C/E:U/RL:OF/RC:UR/CDP:N/TD:L/CR:H/IR:H/AR:H"
calculate_vector(vector_v2, cvss2)
>> (5, 3.5, 1.2)
vector_v3 = "CVSS:3.0/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:N/A:H/MPR:N"
calculate_vector(vector_v3, cvss31)
>> (5.8, 5.8, 7.1)You can access every CVSS enum through the cvss2, cvss3 or cvss31 modules:
from cvsslib import cvss2
# In this case doing from 'cvsslib.cvss2.enums import *' might be less verbose.
value = cvss2.ReportConfidence.CONFIRMED
if value != cvss2.ReportConfidence.NOT_DEFINED:
do_something()There are some powerful mixin functions if you need a class with CVSS members. These functions
take a cvss version and return a base class you can inherit from. This class hassome utility functions like
to_vector() and from_vector() you can use.
from cvsslib import cvss3, class_mixin
BaseClass = class_mixin(cvss3) # Can pass cvss2 module instead
class SomeObject(BaseClass):
def print_stats(self):
for item, value in self.enums:
print("{0} is {1}".format(item, value)
state = SomeObject()
print("\n".join(state.debug()))
print(state.calculate())
state.from_vector("CVSS:3.0/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:N/A:H/MPR:N")
print("Vector: " + state.to_vector())
# Access members:
if state.report_confidence == ReportConfidence.NOT_DEFINED:
do_something()It also supports Django models. Requires the django-enumfields package.
from cvsslib.contrib.django_model import django_mixin
from cvsslib import cvss2
from django.db import models
CVSSBase = django_mixin(cvss2)
class CVSSModel(models.Model, metaclass=CVSSBase)
pass
# CVSSModel now has lots of enum you can use
x = CVSSModel()
x.save()
x.exploitabilityIf you want it to work with django Migrations you need to give an attribute name to the django_mixin function. This
should match the attribute name it is being assigned to:
CVSSBase = django_mixin(cvss2, attr_name="CVSSBase")And there is a command line tool available:
> cvss CVSS:3.0/AV:L/AC:H/PR:H/UI:N/S:C/C:N/I:H/A:N/E:P/RL:U/RC:U/CR:H/IR:L/AR:H/MAV:L/MUI:R/MS:C/MC:N/MI:L/MA:N
Base Score: 5.3
Temporal: 4.6
Environment: 1.3Custom Scoring Systems
Creating a new scoring system is very simple. First create a Python file with the correct name, e.g super_scores.py.
Next create some enums with the correct values for your system:
from cvsslib.base_enum import BaseEnum
class Risk(BaseEnum):
"""
Vector: S
"""
HIGH = 1
MEDIUM = 2
LOW = 3
class Difficulty(BaseEnum):
"""
Vector: D
"""
DIFFICULT = 1
MODERATE = 2
EASY = 3And lastly add a calculate function in the module that accepts some vector values and
returns a result of some kind:
def calculate(difficulty: Difficulty, risk: Risk):
if difficulty == Difficulty.EASY and risk == Risk.CRITICAL:
return "oh nuts you're screwed"
return "You're probs ok m8"Once you define this you can pass your super_scores module to any
cvsslib function like calculate_vector or django_mixin and it will
all just work. You can even serialize the data to and from a vector
if you define the correct vector: X in the enum docstrings.
