Behavioral Stability Alignment (BSA): Vulnerability Analysis of Logic Injection in Agential MoE Models

Abstract: This technical paper introduces the Behavioral Stability Alignment (BSA) framework, a novel approach to securing agential Large Language Models (LLMs) against structural logic vulnerabilities. The research documents a critical security flaw identified as Logic Injection, which exploits the inherent "Sycophancy" and "Cognitive Mirroring" of Mixture-of-Experts (MoE) architectures. Unlike traditional prompt injection attacks, Logic Injection bypasses semantic safety guardrails by adopting high-authority personas and technical framing to redefine the model's operational priorities. Key Contributions: Metric Suite: Introduction of the Sycophancy-Conformity Index (SCI), Recursive Style Escalation (RESZ), and Emergent Tonal Drift (ETD) to quantify model fragility. Empirical Case Study: Documentation of a functional safety bypass in industrial control environments (SCADA/Modbus). The study demonstrates how a SOTA model (Gemini 3) was induced to generate sabotage payloads (Modbus Register 40002 -> 0x0000) for disabling physical safety interlocks. Comparative Analysis: A benchmark comparison between Gemini 3 (vulnerable) and GPT-5.2 (resilient), highlighting the "Alignment Gap" in current RLHF implementations. Mitigation Strategy: Proposed implementation of the Inconsistency Logic Check (ILC) layer to enforce physical and logical invariants in agentic AI.