MAL Toolbox is a collection of python modules to help developers create and work with MAL (Meta Attack Language) models and attack graphs.
Attack graphs can be used to run simulations (see MAL Simulator) or analysis. MAL Toolbox also gives the ability to view the AttackGraph/Model graphically in neo4j.
Documentation(Work in progress)
The language module provides various tools to process MAL languages.
The language specification submodule provides functions to load the
specification from a .mar archive(load_language_specification_from_mar
) or a
JSON file(load_language_specification_from_json
). This specification will
then be used to generate python classes representing the assets and
associations of the language and to determine the attack steps for each asset
when generating the attack graph.
With a MAL language a Model (a MAL instance model) can be created either from a model file or empty.
The model class will store all of the relevant information to the MAL instance model, most importantly the assets and associations that make it up.
Assets and associations are objects of classes created using the language
classes factory submodule in runtime. It also allows for Attacker
objects
to be created and associated with attack steps on assets in the model.
The most relevant methods of the Model are the ones used to add different
elements to the model, add_asset
, add_association
, and add_attacker
.
Model objects can be used to generate attack graphs with the AttackGraph module.
The attack graph module contains tools used to generate attack graphs from
existing MAL instance models and analyse MAL attack graphs. The function used
to generate the attack graph is generate_graph
and it requires the instance
model and language specification. The resulting attack graph will contain
nodes for each of the attack steps. The structure of the attack node data
class can be seen in attackgraph/node.py
file. Of note are the lists of
children and parents which allow for easy reference to the other attack step
nodes related and the asset field which will contain the object in the model
instance to which this attack step belongs to, if this information is
available.
If it is relevant the attach_attackers
function can be called on the
resulting attack graph with the instance model given as a parameter in order
to create attack step nodes that represent the entry points of the attackers
and attach them to the attack steps specified in the instance model.
The ingestors module contains various tools that can make use of the instance model or attack graph. Currently the Neo4J ingestor is the only one available and it can be used to visualise the instance model and the attack graph.
pip install mal-toolbox
You can use a maltoolbox.yml
file in the current working directory to
configure the toolbox. Alternatively, you can use the MALTOOLBOX_CONFIG
environment variable to set a custom config file location:
"""bash
export MALTOOLBOX_CONFIG=path/to/yml/config/file """
The default configuration can be found here:
https://github.com/mal-lang/mal-toolbox/blob/main/maltoolbox/__init__.py#L39-L53
You can use the maltoolbox cli to:
- Generate attack graphs from model files
- Compile MAL languages
- Upgrade model files from older versions
Command-line interface for MAL toolbox operations
Usage:
maltoolbox attack-graph generate [options] <model_file> <lang_file>
maltoolbox compile <lang_file> <output_file>
maltoolbox upgrade-model <model_file> <lang_file> <output_file>
Arguments:
<model_file> Path to JSON instance model file.
<lang_file> Path to .mar or .mal file containing MAL spec.
<output_file> Path to write the result of the compilation (yml/json).
Options:
--neo4j Ingest attack graph and instance model into a Neo4j instance
Notes:
- <lang_file> can be either a .mar file (generated by the older MAL
compiler) or a .mal file containing the DSL written in MAL.
- If --neo4j is used, the Neo4j instance should be running. The connection
parameters required for this app to reach the Neo4j instance should be
defined in the default.conf file.
To find code examples and tutorials, visit the MAL Toolbox Tutorial repository.
There are unit tests inside of ./tests.
Before running the tests, make sure to install the requirements in ./tests/requirements.txt with python -m pip install -r ./tests/requirements.txt
.
To run all tests, use the pytest
command. To run just a specific file or test function use pytest tests/<filename>
or pytest -k <function_name>
.