# Binding Biometrics with AI Agent Identifiers for Delegation of Authority

arXiv: https://arxiv.org/abs/2608.04292 · HTML: https://mlsanity.com/paper/2608.04292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 52 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 19 of 19)

- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)](https://mlsanity.com/paper/2505.02279/) (2025)
- [Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework](https://mlsanity.com/paper/2601.11893/) (2026)
- [Infrastructure for AI Agents](https://mlsanity.com/paper/2501.10114/) (2025)
- [KeyPoint Relative Position Encoding for Face Recognition](https://mlsanity.com/paper/2403.14852/) (2024)
- [A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions](https://mlsanity.com/paper/2402.01968/) (2024)
- [Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing](https://mlsanity.com/paper/1703.05455/) (2017)
- [IDs for AI Systems](https://mlsanity.com/paper/2406.12137/) (2024)
- [Intelligent AI Delegation](https://mlsanity.com/paper/2602.11865/) (2026)
- [GridMind: LLMs-Powered Agents for Power System Analysis and Operations](https://mlsanity.com/paper/2509.02494/) (2025)
- [Identity Management for Agentic AI: The new frontier of authorization, authentication, and security for an AI agent world](https://mlsanity.com/paper/2510.25819/) (2025)
- [The Adoption and Usage of AI Agents: Early Evidence from Perplexity](https://mlsanity.com/paper/2512.07828/) (2025)
- [IronMask: Modular Architecture for Protecting Deep Face Template](https://mlsanity.com/paper/2104.02239/) (2021)
- [Authorization Propagation in Multi-Agent AI Systems: Identity Governance as Infrastructure](https://mlsanity.com/paper/2605.05440/) (2026)
- [OTB-morph: One-Time Biometrics via Morphing](https://mlsanity.com/paper/2302.09053/) (2023)
- [Secure Biometrics: Concepts, Authentication Architectures and Challenges](https://mlsanity.com/paper/1305.4832/) (2013)
- [A2H: Agent-to-Human Protocol for AI Agent](https://mlsanity.com/paper/2602.15831/) (2025)

## Abstract (arXiv)

The proliferation of agentic artificial intelligence (AI) systems has raised serious questions about the accountability for tasks performed by AI agents. Ideally, an AI agent must not be allowed to perform critical tasks without explicit authorization by a human operator. Since biometric recognition is one of the most reliable approaches for authenticating individuals, it has the potential to enable authenticated delegation of authority to AI agents. In this work, we present a framework called BIND, which leverages ideas from the field of biometric cryptosystems, to securely bind biometric data of the human user to the AI agent identity (ID) and authority scope (task-specific constraints) at the time of agent authorization. This token/identifier can be presented by the AI agent to an Identity Auditor, who simultaneously performs biometric authentication and recovers the agent ID and scope, thereby enabling real-time user authentication and establishing a non-repudiable proof of human control and delegation of authority. We also provide a practical implementation of the proposed BIND framework based on face features extracted using standard deep neural network models. To facilitate this implementation, we propose a feature adaptation module that transforms real-valued feature embeddings into fixed-length binary representations suitable for a fuzzy commitment construct based on turbo error correcting codes. Experiments demonstrate the practical feasibility of the proposed face cryptosystem, achieving a True Match Rate of 96% at zero False Match Rate and supporting 1024-bit agent tokens.