MAD-CAT (Meow Attack Data Corruption Automation Tool) is a Python-based solution created by security researcher Karl Biron of Trustwave. It is designed to test and harden environments against data corruption attacks similar to the notorious Meow attacks, which peaked in 2020. MAD-CAT targets multiple vulnerable database platforms, including MongoDB, Elasticsearch, Cassandra, Redis, CouchDB, and Hadoop HDFS. Security professionals can utilize MAD-CAT to perform simulated attacks without causing real harm, helping to raise awareness of vulnerabilities in exposed database instances.
The tool operates in both non-credentialed and credentialed modes, allowing users to conduct single-target tests or bulk attacks via CSV lists. It follows a four-phase process that mirrors the Meow campaign, ensuring accurate simulations that aid in forensic analysis. Users can also use a companion script, fetch_data.py, to verify data states before and after attacks, confirming the tool’s effectiveness. With supported services displayed, the tool promotes community contributions and can be enhanced to accommodate new databases, making it a versatile asset for security professionals looking to bolster their defenses against data corruption threats.
👉 Pročitaj original: Cyber Security News
