AI Security Advancements: EDR Evasion and Google's Mantis Framework
The latest issue of tl;dr sec highlights new techniques for using Large Language Models (LLMs) to extract Endpoint Detection and Response (EDR) logic to evade detection, alongside Google's release of Mantis, a modular toolkit for AI-assisted security reviews. This highlights the dual-use nature of AI in cybersecurity, where LLMs can both accelerate offensive tactics like EDR evasion and automate defensive tasks like finding and patching zero-day vulnerabilities. Google's Mantis is a stack-agnostic toolkit designed to help AI coding agents autonomously find, reproduce, and patch vulnerabilities using a 15-stage pipeline. Meanwhile, attackers are leveraging LLMs to analyze and bypass EDR security controls.
## BACKGROUND
Endpoint Detection and Response (EDR) is a cybersecurity technology that continuously monitors endpoint devices to detect and respond to cyber threats. Google's Mantis is a framework that uses a funnel-style prompt architecture to turn LLMs into security reviewers, enabling them to autonomously handle vulnerability lifecycles.