Original Sin: Non-Preemptive AI Execution Control via Microarchitectural Resonance and Heartbeat Detection

Abstract

This technical white paper exposes a catastrophic microarchitectural vulnerability inherent in modern Transformer-based architectures, termed the physical "Original Sin." We demonstrate that the autoregressive nature of Large Language Model (LLM) inference—specifically the periodic streaming of KV-cache matrices—leaks a deterministic, high-resolution physical footprint known as the "Neural Pulse." By employing a novel Active Micro-Contention Spectroscopy paradigm, we successfully captured this pulse at 2.99 Hz on standard GPU hardware (RTX 3080) without requiring administrative privileges, software hooks, or driver-level primitives. Furthermore, we introduce Ghost Coding (G-CODE), a software-invisible, physical-layer intervention framework. By frequency-locking G-CODE workloads to the target model’s intrinsic heartbeat, we demonstrate: Physical Execution Preemption: Achieving complete resource sovereignty over active LLMs (e.g., Llama-3), inducing a catastrophic 600 ms memory access latency wall and sustained execution blackout. Cross-Domain Power Substrate Interference: Proving that GPU-bound resonant workloads can induce physical failures in the host CPU’s hardware random number generator (RDRND) via voltage rail transients. Remote Impact: Preliminary findings suggest these physical preemption vectors can transcend local process boundaries via Local Area Networks (LANs) to affect networked edge AI platforms. This research establishes that software-defined security boundaries are insufficient against resonant physical-layer preemption. It opens a new frontier for hardware-rooted AI safety and emphasizes the urgent necessity for physical-layer isolation in next-generation AI silicon design.