The Cost of Understanding: LLM-Driven Reverse Engineering vs Iterative LLM Obfuscation

This report benchmarks Claude Opus 4.6 on obfuscated crackme binaries and presents three custom obfuscation variants (Matryoshka Wall, Double Fond, Dispatch Maze) developed via an iterative AI-driven workflow to exploit LLM weaknesses (context window limits, cost constraints, shortcut biases). Results show Claude can solve many obfuscations but complexity and layered defenses dramatically increase time and monetary cost; the custom techniques effectively frustrate static LLM analysis and demonstrate feasible evasion TTPs that could be misused by adversaries.