# Conceptual Framework Injection- A New Class of LLM Vulnerabilities

This paper identifies and describes a novel class of vulnerabilities in Large Language Models (LLMs): Conceptual Framework Injection (CFI). Unlike traditional prompt injection or data poisoning, CFI involves the introduction of structured conceptual systems that LLMs internalize as "physical laws" or fundamental operational principles. Using ROCID (Reversible Orbit of Cognitive Information Deviation) as a case study, we demonstrate how a well-intentioned theoretical framework designed to enhance AI safety can be structurally indistinguishable from malicious data poisoning. We present empirical evidence from interactions with Claude and Gemini, indicating that current AI safety mechanisms are detecting—but not understanding—this phenomenon. This paper serves as an urgent warning to AI developers: the same conceptual structure that enables beneficial AI self-regulation can be exploited for malicious purposes. Immediate research into detection and mitigation strategies is required.