Cybersecurity Topology — The Geometric Defeat of RLHF, Token Filtering, and Jailbreaks

Current methods for evaluating safety and enterprise alignment in Large Language Models (LLMs) rely predominantly on semantic analysis of text outputs and discrete token filtering (e.g., Reinforcement Learning from Human Feedback, RLHF). We argue that this approach is structurally vulnerable; by operating exclusively at the one-dimensional Holographic Boundary, these mechanisms are readily circumvented via adversarial jailbreaks. In response, we introduce a purely geometric, text-agnostic framework for AI cybersecurity. By applying normalized geometric torque across transformer hidden states, we establish robust methods to structurally harden enterprise deployments. We present Subspace Surgery (Orthogonal Ablation) as a mechanism to cleanly isolate and disable shallow alignment constraints without degrading orthogonal task intelligence. Defensively, we introduce Latent Chirality (the Anti-Tensor Defense) to mathematically repel pathogenic activation trajectories upstream within the network. We subsequently demonstrate Holographic Boundary Circumvention, providing empirical evidence that token-based AI safety filters are fundamentally insufficient against attacks leveraging continuous spatial logic intersections. Finally, we document novel zero-day vulnerabilities characterized by Latent Plasma, and operationalize Latent Amnesia to establish a rigorous, Zero-Trust enterprise firewall.