The Defensive Stack is Exposed: LLMs, Reverse Engineering, and the End of Opaque Defense
TrustedSec argues that LLMs materially reduce the cost of reverse engineering defensive products, compressing analysis of five commercial endpoint tools from weeks to days by automating mapping, summarization, and cross-version comparison while humans validate findings. The article focuses on the fact that AV/EDR products commonly ship readable or recoverable on-host decision logic such as YARA-style rules, behavioral engines, Lua scripts, allowlists, prefilters, local ML classifiers, and policy data that can sometimes be exposed in places like the registry. With that material, an analyst can use LLM-assisted workflows for rule extraction, exclusion mining, model and threshold analysis, gap identification, and update diffing to understand what telemetry or behaviors a product actually prioritizes. It also highlights that the same workflow can surface product vulnerabilities in parsing code, IPC interfaces, local SYSTEM services, update mechanisms, kernel callbacks, and tamper-protection logic, making ‘security through obscurity’ a weaker assumption for endpoint defenses.