Cognitive Integrity Security: Securing Human-AI Interaction in Large Language Model Systems

Large language models (LLMs) have expanded far beyond traditional software boundaries, mediating decisions, generating authoritative sounding content, and directly influencing human reasoning across consumer, enterprise, and critical infrastructure domains. The scale and diversity of today's AI landscape-spanning open-weight models, frontier scale systems, local inference runtimes, agentic workflows, and fine-tuned domain models-has created an ecosystem too vast and too fast moving for classical cybersecurity frameworks to contain. Traditional security models were designed for deterministic systems with predictable failure modes. LLMs are probabilistic and context sensitive, capable of producing outputs that appear factual even when they are not. As a result, the most consequential risks of modern AI-including hallucination, deceptive reasoning, contextual leakage, unsafe instruction generation, and cognitive manipulation-do not arise from infrastructure compromise, but from the human-AI interaction layer, where probabilistic model behavior can mislead or manipulate human cognition. This paper introduces Cognitive Integrity Security as a distinct operational security layer designed to address these risks by treating human cognition as a first class security surface. Rather than focusing solely on data, systems, or availability, Cognitive Integrity Security emphasizes the protection of users from harmful or misleading model behavior during real world interaction.