The infamous Meow attack, which devastated unsecured databases since 2020, has resurfaced with renewed force through MAD-CAT (Meow Attack Data Corruption Automation Tool).
This custom-built adversarial simulation tool demonstrates how easily attackers can corrupt data across multiple database platforms simultaneously, highlighting a critical vulnerability that continues to plague modern infrastructure.
The Evolution of Meow
While Meow attack incidents peaked in 2020, Shodan searches still reveal dozens of compromised databases bearing the telltale “-MEOW” signature random alphanumeric strings appended to corrupted data.
Security researchers have now developed MAD-CAT to comprehensively simulate these attacks across six real-world database platforms: MongoDB, Elasticsearch, Cassandra, Redis, CouchDB, and Hadoop HDFS.
Unlike its predecessor, which focused on single-target exploitation, MAD-CAT introduces bulk CSV-based campaigns that enable attackers to corrupt entire database ecosystems in coordinated strikes.
This represents a significant escalation in attack methodology, where defenders lose the sequential detection opportunities that single-target attacks provide.
How MAD-CAT Works
MAD-CAT operates through a systematic four-phase workflow. The tool first connects to target databases in either non-credentialed (for unauthenticated targets) or credentialed (for weak/default credentials) modes.
It then enumerates all databases and collections while deliberately excluding system databases to maximize impact on operational data.
The corruption phase fetches all records and systematically replaces string and numeric fields with ten-character random alphanumeric strings followed by “-MEOW” precisely mirroring the 2020 attack signature.
The tool’s modular architecture uses a factory pattern, allowing researchers to add support for new platforms without modifying core framework code.
Simulations using MAD-CAT demonstrate catastrophic potential in enterprise environments.
In a healthcare scenario spanning all six database platforms, the attack would simultaneously corrupt patient records (MongoDB), eliminate clinical search capabilities (Elasticsearch), destroy IoT telemetry from medical devices (Cassandra), invalidate active user sessions (Redis), eliminate patient portal access (CouchDB), and destroy billing and compliance records (Hadoop HDFS).
This coordinated assault represents what defenders face in modern attacks: not sequential exploitations but synchronized, multi-platform data destruction capable of crippling entire organizations within minutes.
Shodan trend analysis reveals a positive development. Elasticsearch’s compromises dropped from 13,000 in late 2020 to just seven by September 2025 an 85% reduction.
MongoDB instances declined from 6,000 to 26, while CouchDB fell from 280 to three compromised cases. This reflects industry response through mandatory authentication in newer database versions and heightened security awareness.
However, the persistence of compromised instances five years after the initial campaign demonstrates that security remains uneven. Legacy systems and organizational negligence continue to create vulnerability windows.
The Meow lesson remains unambiguous: misconfiguration kills. Organizations must enforce authentication by default, rotate credentials regularly, segment database access, and maintain comprehensive backups.
Security solutions offering vulnerability detection, default credential identification, and patch verification provide essential defense layers against similar attacks.
As the threat landscape evolves, MAD-CAT serves as a sobering reminder that well-documented attack vectors continue to claim victims through inadequately secured infrastructure.
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