fallrisk-ai
Published March 26, 2026 | Version v1
Technical note Open

Agent Identity Is Not Model Identity — Why authenticating the software is not the same as proving which model is actually computing

Authors/Creators

  • 1. Fall Risk Research

Description

Modern AI deployment stacks authenticate artifacts, credentials, and agents. They do not verify which neural network is actually computing at inference time. This technical note identifies the distinction between agent identity and model identity, presents a four-question taxonomy for the identity surface of deployed AI systems, and situates recent public incidents within the resulting gap. It draws on the formal admissibility framework and frontier-scale measurement results from the accompanying research series.

This is a technical note, not a numbered entry in the research series. 

The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running

  1. Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275)

  2. Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711)

  3. Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608)

  4. Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071)

  5. 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)

  6. Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116)

  7. Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) 

  8. Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540)

  9. Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) 

  10. Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807)

  11. Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634)

  12. Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857)

  13. Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019)

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).

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Additional details

Identifiers

URL
https://fallrisk.ai

Is supplement to
Preprint: 10.5281/zenodo.18704275 (DOI)
Preprint: 10.5281/zenodo.18818608 (DOI)
Preprint: 10.5281/zenodo.18872071 (DOI)
Preprint: 10.5281/zenodo.18907293 (DOI)
Preprint: 10.5281/zenodo.19008116 (DOI)
Preprint: 10.5281/zenodo.19055966 (DOI)
Preprint: 10.5281/zenodo.19058540 (DOI)
Preprint: 10.5281/zenodo.19099911 (DOI)
Preprint: 10.5281/zenodo.19118807 (DOI)
Preprint: 10.5281/zenodo.19216634 (DOI)
Preprint: 10.5281/zenodo.18776711 (DOI)
Technical note: 10.5281/zenodo.19275524 (DOI)
Preprint: 10.5281/zenodo.19298857 (DOI)
Preprint: 10.5281/zenodo.19383019 (DOI)
Is supplemented by
Software: 10.5281/zenodo.18930621 (DOI)
Technical note: 10.5281/zenodo.19342848 (DOI)
Technical note: 10.5281/zenodo.20019127 (DOI)

Software

Repository URL
https://github.com/fallrisk-ai/trustfall-lite
Development Status
Active