The Model State Attestation Framework: Evidence-Based Governance for Foundation Models

Foundation models now occupy positions of consequential authority across credit adjudication, clinical diagnostics, public resource allocation, and autonomous trading. Yet no unified system exists to cryptographically prove that a specific inference was generated by a verified model version operating in a compliant internal state. Current governance artifacts—model cards, training logs, static benchmarks—are descriptive, self-reported, and fundamentally non-verifiable. This paper proposes the Model State Attestation Framework (MSAF), a three-tier architecture composing hardware-rooted trust (TEE platform attestation), signed continuous internal invariant monitoring (gradient integrity, entropy floor, structural coherence, Lyapunov stability, distributional drift), and periodic deep verification anchors (zkML inference proofs, provenance hashing, contamination detection). A survey of 200+ papers (2022–2026) confirms that while each component exists individually, no system yet composes them into a unified attestation artifact for foundation-scale models. The framework maps composed artifacts to specific evidence requirements across five regulatory domains: EU AI Act (zero harmonized standards published as of February 2026), FDA SaMD (PCCP framework), Federal Reserve SR 11-7, US Federal Procurement (M-26-04), and AI Insurance (projected $4.8B market by 2032). Theoretical ceilings are documented honestly: Rice's theorem bounds the gap between process attestation and behavioral guarantees, the Impossibility Sandwich constrains formal verification of useful models, and TEE physical side-channel attacks (TEE.Fail, HyperTheft, CVE-2024-56161) limit hardware trust assumptions. The honest framing: MSAF provides probabilistic risk reduction and accountability infrastructure—analogous to financial auditing, medical device regulation, and food safety inspection—not behavioral safety guarantees. The building blocks exist, the regulatory pull is strong, and the standards window is open.