We’re losing — but it can’t get any worse, right?

This blog post explains how attackers leverage large language models to produce polymorphic malware that generates malicious code at runtime, defeating signature-based and many behavioral defenses. It reviews demonstrations and ecosystem signals—including CyberArk and SentinelOne/CrowdStrike research, underground AI services such as WormGPT, and a proof-of-concept keylogger (BlackMamba)—and offers a code example showing how an LLM can synthesize and execute payloads in-memory. The author outlines why traditional telemetry, static signatures, and some ML detectors struggle with this dynamic threat and previews follow-up analysis on C2, obfuscation, and AI-enhanced social engineering.