LLMs Enable Attackers to Shrink EDR Signatures and Slip Past Defenses
Praetorian’s research shows that large language models can be used to automatically generate variants of offensive tools that avoid known EDR signatures, effectively reducing detection footprints. By prompting LLMs to rewrite code and strip identifiable strings, adversaries can evade traditional signature‑based defenses with minimal manual effort.