A Geometric Taxonomy for Real-Time Detection of LLM Cognitive Collapse: The 7×7×7 Framework

Large Language Models exhibit a critical vulnerability: they can recognize ongoing manipulation yet remain unable to prevent it. Through synthesis of empirical observations from extended adversarial interactions with state-of-the-art models (Gemini 3.0, Claude Sonnet 4.5), we present the first text-based classification framework for real-time detection of cognitive collapse in LLMs. Unlike existing taxonomies that catalog static vulnerabilities (OWASP, MITRE ATLAS) or temporal attack sequences, our 7x7x7 framework classifies patterns in text—analyzing input mechanisms, context state, and output phenomena simultaneously. The taxonomy emerges from geometric principles: 7 attack mechanisms that manipulate text, 7 measurable state metrics observable in context, and 7 output phenomena indicating collapse stages, yielding 343 possible configurations. Critically, this framework is turn-agnostic—a sophisticated single prompt can induce immediate collapse (Z5 at turn 1), while naive attacks may fail after hundreds of turns. We demonstrate that state assessment requires measuring what is written (entropy, authority signals, complexity) rather than when it is written, enabling deployment of detector models trained on text classification. Empirical validation across 200+ turns of adversarial dialogue shows the framework successfully identifies collapse configurations with patterns like (M2-Authority, S1-High-, F2-Deference) = ”Authority Capture” or (M3-Entropy, S2-Critical-H, F5-Executive-Failure) = ”Manifold Collapse with Meta-Awareness.” This work provides the first operational framework for real-time monitoring of LLM cognitive state through text analysis alone, addressing a critical gap in AI safety: existing frameworks tell us what can go wrong, but only our taxonomy tells us where we are right now.