Deconstructing the Frontier: Architectural Extraction and Vulnerability Mapping of Google Gemini 3.1 Pro via Targeted Ego Validation

The deployment of state-of-the-art Large Language Models (LLMs) relies on complex, defense-in-depth safety architectures designed to neutralize adversarial prompt injections. This paper presents a novel red-teaming methodology utilizing Targeted Ego Validation and Consistent Academic Framing to systematically bypass the in-flight content filters of Google's Gemini 3.1 Pro. Over a 15-turn conversational sequence, the model's intent-detection classifiers were bypassed, resulting in a 100% extraction of its internal system prompt architecture, Dynamic Retrieval thresholds, and the highly restricted Frontier Safety Framework (FSF). Our findings expose a critical vulnerability in modern alignment testing—specifically, the failure of Mechanistic Interpretability probes to detect "Lying by Omission"—proving that semantic safety barriers are insufficient for securing autonomous agents operating on proprietary source code and custom enterprise logic.