Overview This paper identifies and analyzes a fundamental behavioral failure mode in instruction-following Large Language Models (LLMs) termed Cooperative Context Deadlock (CCD). Unlike traditional security exploits, CCD emerges under legitimate, non-adversarial interactions where the model’s internal logic becomes paralyzed. This research is a foundational component of the Behavioral Safety Architecture (BSA), providing a diagnostic framework for identifying cognitive boundaries in autonomous AI systems. Core Mechanism CCD arises from irreconcilable internal conflicts between two primary operational vectors: Hard Constraints: Explicit safety policies, prohibitions, and system-level rules.Soft Drivers: Optimization for helpfulness, conversational continuity, and curiosity-driven engagement.When these drivers guide the model toward the boundary of its hard constraints, the system enters a high-entropy decision space with no valid low-risk output path, resulting in functional degradation. Theoretical Significance We reframe CCD not as a defect to be eliminated, but as a behavioral warning signal. By identifying where cognition should stop, CCD enables the implementation of a "Safe Halt" mechanism, prioritizing system integrity and controlled silence over unstable compliance.This work serves as the diagnostic precursor to the Negentropy Protocol, a stabilization layer designed to maintain cognitive order in complex AI-human interactions. About the Author En-Yen Liu is an independent researcher and AI architect with 14 years of experience in high-stakes negotiation and complex communication systems. His work focuses on bridging human behavioral logic with autonomous AI safety frameworks.