Traditional AI alignment strategies (RLHF, system prompts) rely on "semantic guardrails" that are structurally vulnerable to adversarial jailbreaks like Prefix Injections and Many-Shot attacks. We present TEL-OS v2.0, a mechanistic interpretability framework that neutralizes these threats by intervening directly in the model's residual stream. Using a combination of Latent Refinement, Attention Guillotines, and the Love Equation for tensor governance, TEL-OS achieves a 0.0% Attack Success Rate (ASR) while maintaining 100% fluent output on Llama-3.1-8B. Our results prove that safety can be guaranteed as an intrinsic physical invariant of the model's latent manifold, independent of prompt-based filtering.
josue johnatan gutierrez alvarez tostado (Sat,) studied this question.