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A Python SDK for foreseeti's securiCAD Vanguard

Project description

securiCAD Vanguard SDK

Automated threat modeling and attack simulations in your CI/CD pipeline

A Python SDK for foreseeti's securiCAD Vanguard. Sign up for an account at AWS Marketplace.

Getting started

Sign up for a securiCAD Vanguard account

Go to AWS Marketplace and sign up to securiCAD Vanguard and verify your account.

Download and setup the SDK

Clone this repository and install the required third-party libraries.

git clone
cd securicad-vanguard-sdk
pip install -r requirements.txt

Get the required AWS credentials

The securiCAD Vanguard SDK requires AWS credentials to be able to fetch data from AWS and run the simulations on your environment. The easiest way is to create an IAM User with the required permissions and generate access keys for that IAM User:

Cross-account role access and local model generation will be available soon.

Run your first simulation

The following snippet runs a simulation on an AWS environment where the high value asset is an EC2 instance with id i-1a2b3c4d5e6f7 and prints the results. Please note, never store your credentials in source code, this is just an example.

import json
from securicad import vanguard

# Your vanguard credentials
email = "your vanguard email"
password = "your vanguard password"

# AWS credentials for IAM user 
access_key = "aws access key id"
secret_key = "aws secret key"
region = "your aws region" # e.g., us-east-1

# Create an authenticated vanguard client
client = vanguard.client(username=email, password=password)

# Generate a model from your AWS environment
model = client.get_model(access_key=access_key, secret_key=secret_key, region=region)

# Set high value assets

# Simulate and print the results
results = client.simulate(model, profile=vanguard.Profile.CYBERCRIMINAL)
print(json.dumps(results, indent=2))

If you wish to run securiCAD Vanguard with a local file, replace the client.get_model() call in the above example with:

import json

with open('data.json', mode='r', encoding='utf-8') as json_file:
    data = json.load(json_file)
model = client.get_model(data=data)

The results will be returned as a dict with your high value asset identifiers as keys. For example:

    "buckets": {},
    "dbinstances": {},
    "instances": {
        "i-1a2b3c4d5e6f7": {
            "object_name": "web-server",
            "probability": 0.5,
            "ttc": 59

Check out for a more detailed example.


Below are a few examples of how you can use boto3 to automatically collect name or ids for your high value assets.

Get EC2 instance ids

Get all EC2 instance ids where the instance is running and has the tag owner with value erik.

import boto3

session = boto3.Session()
ec2 = session.resource('ec2')

# List all running EC2 instances with the owner-tag erik
instances = ec2.instances.filter(
        {"Name": "tag:owner", "Values": ["erik"]},
        {'Name': 'instance-state-name', 'Values': ['running']}
# Get the instance-id of each filtered instance
instance_ids = [ for instance in instances]

Get RDS instance identifiers

Get all RDS instances and their identifiers.

import boto3

session = boto3.Session()
rds = session.client('rds')

# Get all RDS instance identifers with a paginator
dbinstances = []
paginator = rds.get_paginator('describe_db_instances').paginate()
for page in paginator:
    for db in page.get('DBInstances'):

Get S3 buckets

Get all S3 buckets where the bucket name contains the string erik.

import boto3

session = boto3.Session()
s3 = session.resource('s3')

# Get all s3 buckets where `erik` is in the bucket name
buckets = []
for bucket in s3.buckets.all():
    if 'erik' in


Additional information can be found at:


"The code in this project is licensed under Apache-2.0."

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