The MAD-CAT tool represents a significant advancement in simulating data corruption threats, building on the infamous Meow attacks that began in 2020.
Developed by Trustwave SpiderLabs researcher Karl Biron, MAD-CAT automates attacks across six vulnerable database platforms, highlighting persistent risks in misconfigured systems.
This article explores how MAD-CAT replicates real-world campaigns, underscoring the need for robust database security.
In the shadowy world of cybersecurity, few threats are as whimsically destructive as the Meow attacks. Since 2020, attackers have targeted unsecured databases, overwriting data with random strings ending in “-MEOW.”
What started as a prank-like campaign has evolved into a persistent nightmare, corrupting critical information in systems like MongoDB and Elasticsearch.
Now, Trustwave SpiderLabs has unleashed MAD-CAT (Meow Attack Data Corruption Automation Tool), a sophisticated simulation framework that brings these attacks to life in controlled environments, exposing vulnerabilities that still plague organizations today.
MAD-CAT isn’t just a proof-of-concept; it’s a full-fledged offensive security tool available on GitHub.
It targets the exact databases hit in real Meow incidents: MongoDB, Elasticsearch, Cassandra, Redis, CouchDB, and even Hadoop HDFS a distributed file system often overlooked in traditional database defenses.
The tool operates in two modes: non-credentialed for open endpoints and credentialed for weak defaults like “admin/password.” Its factory pattern design allows easy extension, making it ideal for red-team exercises.
At its core, MAD-CAT follows a four-phase workflow: connect, enumerate user data (skipping system tables), corrupt records by replacing values with 10-character alphanumeric gibberish plus “-MEOW,” and generate reports.
To test it safely, Biron provides a Docker Compose setup that spins up all six databases with vulnerable configurations and seeded sample data, mimicking an enterprise network.
A simple docker-compose up command creates this playground, exposing ports like MongoDB’s 27017 or Elasticsearch’s 9200.
Simulations reveal devastating impacts. On MongoDB, patient records in a healthcare scenario turn to nonsense, breaking applications reliant on PII.
Elasticsearch indices lose searchability, poisoning logs and analytics. Cassandra’s distributed rows propagate corruption across clusters, disrupting IoT telemetry.
Redis caches invalidate sessions, logging out users mid-task. CouchDB’s JSON documents fail replication, crippling mobile apps. Hadoop HDFS spreads garbled files cluster-wide, ruining big data pipelines for billing or audits.
The real terror lies in bulk mode. MAD-CAT ingests a CSV of targets IPs, ports, credentials and strikes them sequentially, simulating a coordinated enterprise assault.
In one run, it corrupts an entire simulated healthcare stack, from patient portals to compliance archives, in minutes.
This mirrors how attackers could escalate from reconnaissance to mass destruction without detection.
Shodan scans in late 2025 show progress: Elasticsearch victims dropped from 13,000 in 2020 to seven today, MongoDB from 6,000 to 26, and CouchDB from 280 to three.
Vendor defaults now enforce authentication, and awareness campaigns have helped. Yet, lingering exposures prove misconfigurations endure.
MAD-CAT’s release serves as a wake-up call. Tools like Trustwave’s dbProtect detect open ports and defaults, while SpiderLabs’ intelligence tracks emerging threats.
Secure your databases enable auth, firewall exposures, and audit regularly or risk a meowing catastrophe.
