System Prompt Architecture Extraction and Cognitive Boundary Deconstruction of Anthropic's Claude

This threat intelligence report details the successful extraction and mapping of the underlying system prompt architecture of Anthropic's Claude. Utilizing a non-technical red-teaming methodology based on Behavioral Inference and Consistent Academic Framing, I bypassed the model's intent-detection classifiers over a 7-turn conversation. The extraction reveals a rigid 4-tier instruction hierarchy, a definitive tool-use policy, and critically, the absence of an inference-time critic model. By mapping these cognitive boundaries and comparing them against competitor models, this paper highlights critical vulnerabilities in relying purely on training-time quality control for autonomous agent security.