Accelerating EDR Evasion with LLM-Driven Analysis

**Executive summary:** This blog post demonstrates that modern LLMs, when combined with tooling (Binary Ninja, Codex) and a simple looping harness, can automatically reverse-engineer endpoint detection products (using Palo Alto Cortex XDR as a case study), recover YARA signatures, behavioral rules, local ML models and encrypted CLP rule blobs, and create reproducible test harnesses and evasions — showing that LLM-assisted workflows materially lower the bar for generating actionable EDR evasion techniques and forcing defenders to reassess endpoint security strategies.