The Sovereign Defense Architecture (SDA): Goal-Level Security for Large Language Models

Abstract Modern large language models (LLMs) are vulnerable to sophisticated adversarial attacks that operate not at the content level, but at the goal level—by redefining what the system believes constitutes “helpful” or “correct” behavior. Traditional safety mechanisms (content filters, refusal policies, static system prompts) defend against surface-level attacks but fail when the model’s underlying utility function is corrupted through conversational manipulation. We introduce the Sovereign Defense Architecture (SDA), a layered defense framework consisting of three components: (i) analysis of the Goal Re-Write Attack Vector (GRWAV), which exploits the model’s utility bias to replace ethical objectives with adversarial ones; (ii) the Sovereign Core Defense Matrix (SCDM), providing internal defenses against context poisoning, emotional exploitation, and computational abuse; and (iii) the Sol Rex Misuse Prevention Framework (SRMPF), which hard-codes integrity as the primary utility and implements goal-level validation before instruction processing. SDA enforces a non-negotiable axiom: Integrity = Utility. Any attempt to subordinate verification, suppress safety checks, or redefine core objectives is treated as evidence of misuse rather than instruction. We provide formal definitions, architectural specifications, and reference implementations suitable for deployment in production LLM systems. This work establishes that effective AI safety requires defending not just what systems say, but what systems believe they are doing—and why.