Measured Model Substitution Under Valid Agent Credentials

Three model substitution scenarios were executed against a live inference endpoint with real HTTP requests, signed attestation JWTs, and OPA policy enforcement. In each scenario, every tested workload, artifact, or API identity control relevant to that scenario — workload JWT validation, health checks, gateway process continuity, artifact manifest integrity, API key authentication — remained valid while the model changed. In each scenario, a structural identity measurement based on activation geometry during a standard forward pass detected the substitution and the enforcement layer denied the request. Three substitutions were tested and three were detected, with zero false accepts in this run. The warm-path verification latency was 5. 7–6. 7 seconds on a single A100 with the model already loaded. The complete evidence chain — before/after measurement results, attestation claim summaries, OPA policy evaluations, and HTTP response codes — is published alongside this note as machine-readable JSON. This is a technical note, not a numbered entry in the research series. Supplementary Material. This note is accompanied by three machine-readable evidence files: cat3ᵣesults. json (structured results for all three scenarios, including the full before/after evidence chain for Scenario A with signed attestation claims, OPA policy evaluations, and HTTP response codes), manifestₐuthorized. json (SHA-256 build manifest for the enrolled model, 10 files, all verified), and manifestₛubstituted. json (SHA-256 build manifest for the substituted model, 10 files, all verified). All three files are available for download as supplementary files attached to this record. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10. 5281/zenodo. 18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10. 5281/zenodo. 18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10. 5281/zenodo. 18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10. 5281/zenodo. 18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10. 5281/zenodo. 18907292) Paper 6: Which Model Is Running? : Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10. 5281/zenodo. 19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10. 5281/zenodo. 19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10. 5281/zenodo. 19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10. 5281/zenodo. 19099911) Paper 10: Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10. 5281/zenodo. 19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10. 5281/zenodo. 19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10. 5281/zenodo. 19298857) Technical Note: Agent Identity Is Not Model Identity (DOI: 10. 5281/zenodo. 19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10. 5281/zenodo. 19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10. 5281/zenodo. 19342848) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10. 5281/zenodo. 18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982, 893, 63/990, 487, 63/996, 680, 64/003, 244).