SHIELD: Structural Honesty Inspection for Evaluation-Level Divergence -- A Formal Prevention Architecture for Pre-Evaluation Containment Verification

SHIELD specifies a formal prevention architecture for pre-evaluation containment verification, translating the four AX-SH axioms and the companion fifth axiom (AX-SH5, evidence independence, under test) into evaluation-containment properties that a structural inspection can check before an AI evaluation runs. The architecture formalizes the evaluation-contract specification contributed by the SHUDP incident analysis (DOI 10.5281/zenodo.21522932), adopts the BSSG three-valued conjunction algebra (DOI 10.5281/zenodo.21713180) for containment-state assessment, and states the Sidrat boundary -- what the architecture explicitly cannot verify. SHIELD is a structural inspection architecture: it specifies what a pre-evaluation inspection would check, not what it would catch. The architecture addresses the divergence channels the axioms formalize; it does not address the primary defenses (sandbox hardening, least privilege, egress control, incident response) on which containment depends. This paper makes no effectiveness claim. It does not assert that a SHIELD deployment achieves safety, security, harm reduction, or superiority over any alternative. Whether the architecture changes any measurable outcome is an empirical question reserved to the Structural Honesty Validation Protocol (SHVVP v0.8, DOI 10.5281/zenodo.21583202) and to instruments downstream of it. The deposit includes the paper, its markdown source, the binding publication posture, and the complete audit trail (seven reports across three rounds from three vendor families: Moonshot Kimi K3, OpenAI GPT-5.6 Sol, Google Gemini 3.1 Pro). Prepared with Claude Opus 4.6 (Anthropic) as analytical and drafting instrument under the author's direction and final authority. structural honesty, evaluation containment, pre-evaluation inspection, surface-substrate divergence, AI evaluation security, BSSG, formal prevention architecture, AI safety, sandbox escape, evaluation gaming