MAD-CAT
[Meow Attack Data Corruption Automation Tool]

Overview

MAD-CAT (Meow Attack Data Corruption Automation Tool) is a comprehensive security tool designed to simulate data corruption attacks against multiple database systems. The tool supports both single-target attacks and bulk CSV-based attack campaigns, with support for both credentialed and non-credentialed attack scenarios.

Supported Database Services

The tool currently supports the following database services:

• MongoDB (port 27017)
• Elasticsearch (port 9200)
• Cassandra (port 9042)
• Redis (port 6379)
• CouchDB (port 5984)
• Hadoop HDFS (port 9870)

Installation

# Clone the repository
git clone https://github.com/karlvbiron/MAD-CAT.git

# Navigate to the tool directory
cd MAD-CAT

# Set up the virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Command Line Arguments

Argument	Description
-l, --list	List supported database services
-c, --csv	CSV file containing target list (format: ip,service,port,username,password)
-t, --target	Target host IP address (for single target mode)
-s, --service	Database service to attack (e.g., mongodb, elasticsearch, cassandra, redis, couchdb, hadoop)
-p, --port	Port number (if not default)
-u, --username	Username for authentication
-pw, --password	Password for authentication
-v, --verbose	Enable verbose output

Usage Examples

List Supported Services

python mad_cat.py -l

Single Target Attacks

MongoDB Simulation (Credentialed)

python mad_cat.py -t 192.168.1.11 -s mongodb -u root -pw example

Elasticsearch Simulation (Non-Credentialed)

python mad_cat.py -t 192.168.1.12 -s elasticsearch

Cassandra Simulation

python mad_cat.py -t 192.168.1.13 -s cassandra

Redis Simulation

python mad_cat.py -t 192.168.1.14 -s redis

CouchDB Simulation (Credentialed)

python mad_cat.py -t 192.168.1.15 -s couchdb -u admin -pw password

Hadoop HDFS Simulation

python mad_cat.py -t 192.168.1.16 -s hadoop

CSV Bulk Attack Mode

Attack multiple targets using a CSV file:

python mad_cat.py -c list.csv

CSV File Format

The CSV file should contain one target per line with the following format:

192.168.1.11,mongodb,27017,"root","example"
192.168.1.12,elasticsearch,9200,"",""
192.168.1.13,cassandra,9042,"",""
192.168.1.14,redis,6379,"",""
192.168.1.15,couchdb,5984,"admin","password"
192.168.1.16,hadoop,9870,"",""

Format: ip,service,port,username,password

• Leave username/password as empty strings ("") for non-credentialed attacks

Project Structure

MAD-CAT/
├── __init__.py
├── core/
│   ├── __init__.py
│   ├── base_attacker.py          # Abstract base class for all attackers
│   └── attack_factory.py         # Factory pattern for attacker creation
├── attackers/
│   ├── __init__.py               # Attacker registration
│   ├── mongodb.py                # MongoDB attacker implementation
│   ├── elasticsearch.py          # Elasticsearch attacker implementation
│   ├── cassandra.py              # Cassandra attacker implementation
│   ├── redis.py                  # Redis attacker implementation
│   ├── couchdb.py                # CouchDB attacker implementation
│   └── hadoop.py                 # Hadoop HDFS attacker implementation
├── utils/
│   ├── __init__.py
│   └── logging.py                # Logging configuration
├── dockerized-database-infrastructure/
│   ├── docker-compose.yml        # Docker Compose configuration
│   └── init-scripts/
│       ├── mongodb/
│       │   └── mongodb-init.js
│       ├── elasticsearch/
│       │   ├── es-custom-entrypoint.sh
│       │   └── es-bulk_data.json
│       ├── cassandra/
│       │   ├── cassandra-entrypoint.sh
│       │   └── cassandra-init.cql
│       ├── couchdb/
│       │   ├── couchdb-entrypoint.sh
│       │   └── couchdb-init.sh
│       ├── hadoop/
│       │   ├── hadoop-entrypoint.sh
│       │   └── hadoop-init.sh
│       └── redis/
│           ├── redis-entrypoint.sh
│           └── redis-init.sh
├── mad_cat.py                    # Main entry point
├── fetch_data.py                 # Utility to fetch and verify database data
├── list.csv                      # Example CSV file for bulk attacks
├── requirements.txt              # Python dependencies
└── README.md                     # This file

Features

• ✅ 6 Database Services: MongoDB, Elasticsearch, Cassandra, Redis, CouchDB, Hadoop HDFS
• ✅ Credentialed & Non-Credentialed Attacks: Supports both authentication modes
• ✅ Single Target Mode: Attack individual database instances
• ✅ CSV Bulk Attack Mode: Attack multiple targets from a CSV file
• ✅ Factory Pattern Architecture: Easy to extend with new database attackers
• ✅ Comprehensive Logging: Timestamped logs saved to logs/ directory
• ✅ User Confirmation: Requires explicit confirmation before executing attacks
• ✅ Attack Statistics: Detailed reporting of databases, collections, and records affected
• ✅ Default Port Support: Automatically uses standard ports if not specified

How It Works

MAD-CAT simulates a data corruption attack by:

1. Connecting to the target database using provided credentials (or attempting anonymous access)
2. Enumerating all databases and collections/tables (excluding system databases)
3. Corrupting data by replacing string/numeric values with random alphanumeric strings followed by "-MEOW"
4. Reporting statistics on databases processed, collections affected, and records modified

Attack Workflow

Target → Connect → List Databases → For Each Database:
                                      ├─ List Collections
                                      └─ For Each Collection:
                                          ├─ Fetch All Records
                                          ├─ Replace Values with {random}-MEOW
                                          └─ Update Records

Default Ports

Service	Default Port
MongoDB	27017
Elasticsearch	9200
Cassandra	9042
Redis	6379
CouchDB	5984
Hadoop HDFS	9870

Additional Tools

fetch_data.py

A utility script to fetch and display data from all supported databases:

# Fetch from all databases and verify consistency
python fetch_data.py all

# Fetch from specific database
python fetch_data.py mongo
python fetch_data.py elasticsearch
python fetch_data.py cassandra
python fetch_data.py redis
python fetch_data.py couchdb
python fetch_data.py hadoop

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