Iterating the Semantic Firewall Theorized Under the Semantic Field Mechanics Framework with Mistral AI to Address Potential PPI via RAG Pipelines
Abstract
This white paper presents the first empirical implementation of the Semantic Firewall (SF), a pre-retrieval cybersecurity layer derived from the Semantic Field Mechanics (SFM) framework. SFM proposes that meaning in large language models (LLMs) behaves as curvature within context space, allowing semantic coherence and security to be managed through geometric rather than statistical principles. The experiment applied the SF Directive to Mistral AI's Le Chat to evaluate whether it could prevent Poisonous Prompt Injection (PPI) during Retrieval-Augmented Generation (RAG) while preserving full retrieval functionality. Through iterative testing, the firewall evolved from over-restricted to curvature-balanced configurations, successfully maintaining retrieval access while neutralizing all potential embedded directives. The results demonstrate that adaptive field constraints can isolate external attractors before retrieval and maintain coherence across architectures, establishing the Semantic Firewall as a model-agnostic, geometry-based safeguard for AI systems.
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