MAD-CAT (Meow Attack Data Corruption Automation Tool) targets MongoDB, Elasticsearch, Cassandra, Redis, CouchDB, and Hadoop HDFS, exactly the systems hit in the original wave.
This persistent threat inspired security researcher Karl Biron of Trustwave to create MAD-CAT, a Python-based tool for simulating these destructive campaigns across six vulnerable database platforms.
While the notorious Meow attacks peaked in 2020, wiping thousands of exposed databases with strings ending in “-MEOW,” Shodan scans in 2025 still uncover dozens of lingering victims.
Available on GitHub, MAD-CAT enables defenders to test and harden environments against data corruption without real harm.
It operates in non-credentialed mode for open instances or credentialed mode for weak-auth setups, mimicking opportunistic exploits. Users can run single-target tests or bulk attacks via CSV lists, ideal for mass-scanning simulations. The factory pattern design allows easy extension for new databases, promoting community contributions.
Running --help displays options like service selection (-s), ports (-p), and verbose output (-v). The --list flag shows supported services, emphasizing Hadoop’s inclusion as a file system often treated like a database in enterprises.
MAD-CAT: Meow Attack Tool
MAD-CAT follows a four-phase process: connect to the target, enumerate user databases and collections (skipping system ones), fetch records, and overwrite strings/numerics with 10-character random alphanumerics plus “-MEOW”.
This replicates the real campaign’s signature, ensuring simulations match forensic evidence from over 25,000 affected instances. A companion fetch_data.py script verifies pre- and post-attack states, pulling contents by service or all at once, reads the advisory.
To streamline setups, MAD-CAT bundles a Docker Compose file launching all six databases with vulnerable configs and seeded sample data via init scripts.
The command sudo docker-compose up creates a bridged network, persistent volumes, and initializes services sequentially, confirming readiness with “done” statuses.
Checking sudo docker ps -a exposes ports like MongoDB’s 27017 and Elasticsearch’s 9200, simulating an interconnected enterprise setup for holistic testing.
MongoDB, a schema-flexible NoSQL store for apps and IoT data, remains a prime target due to common misconfigurations. Initial fetch_data.py mongo reveals clean documents.
Launching python mad_cat.py -t 192.168.1.11 -s mongodb -p 27017 -u root -pw example connects, enumerates collections, and corrupts records seamlessly, processing three collections without errors. Post-attack fetch shows all values garbled with “-MEOW,” crippling apps handling PII or logs and risking compliance breaches.
Elasticsearch, core to ELK stacks for logs and search, suffers from index poisoning from corruption, breaking analytics or e-commerce features.
Pre-attack fetch dumps intact JSON; the tool via python mad_cat.py -t 192.168.1.12 -s elasticsearch -p 9200 -u admin -pw secret rewrites documents, leaving junk-filled indices.
Cassandra, a high-throughput wide-column store for big data, sees rows overwritten across clusters, propagating mayhem in telecoms or IoT. Commands like python mad_cat.py -t 192.168.1.13 -s cassandra -p 9042 update CQL tables, verified by post-fetch showing “-MEOW” everywhere.
MAD-CAT underscores the need for authentication, firewalls, and monitoring on exposed databases. As Meow echoes linger, tools like this empower proactive defense.
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