The SWAT Architecture: Counter-Engineering Autonomous AI Reverse-Engineering via Stochastic Intermittent Reinforcement and Temporal Tarpits

Automated Reverse-Engineering (ARE) pipelines leveraging Large Language Model (LLM) Agents have significantly lowered the barrier to intellectual property theft and automated vulnerability discovery. Traditional software obfuscation techniques fail against these systems as AI excels at dead-code elimination and pattern matching. This paper presents the SWAT (Stochastic Whiplash & Asymmetric Tarpit) Architecture, a paradigm shift from traditional local code obfuscation to cloud-native cognitive and economic defense. SWAT operates as an asymmetric trap that turns an AI Agent’s algorithmic efficiency into a financial and computational liability. By combining a hidden Zero-Knowledge Gateway with stochastic time-dilation loops (111–115s) and a Schrödinger Odd-Even text-slicing engine, SWAT forces autonomous attackers into a state of Cognitive Whiplash—oscillating their internal confidence metrics violently between 30% and 96%. Experimental and theoretical models demonstrate that SWAT successfully paralyzes offensive LLM parsing heuristics, pollutes the context window with semantic noise, and inflicts an exponential increase in Input Token billing, effectively bankrupting the attacker’s operational budget at near-zero defense cost.