Composable Attestation: A Generalized Framework for Continuous and Incremental Trust in AI-Driven Distributed Systems

• URL: http://arxiv.org/abs/2603.02451v1
• Date: Mon, 02 Mar 2026 22:45:26 GMT
• Title: Composable Attestation: A Generalized Framework for Continuous and Incremental Trust in AI-Driven Distributed Systems
• Authors: Sheng Sun, Sarah Evans,
• Abstract summary: This paper presents composable attestation as a generalized cryptographic framework for Continuous and Incremental Trust in Distributed Systems.<n>We establish a rigorous mathematical foundation which is defining core properties of such attestation systems.<n>The framework's utility extends to applications such as secure AI model integrity verification, federated learning, and runtime trust assurance.
• Score: 4.2822349607372265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents composable attestation as a generalized cryptographic framework for Continuous and Incremental Trust in Distributed Systems,such as Artificial Intelligence (AI) computation, and Open Source Software (OSS) supply chain verification. We establish a rigorous mathematical foundation which is defining core properties of such attestation systems: composability, order independence, transitivity, determinism, inclusion, and dynamic component verification. In contrast to traditional attestation methodologies relying on monolithic verification, composable attestation facilitates modular, scalable, and cryptographically secured integrity verification adaptable to evolving system configurations. This work introduces generalized attestation proof generation and verification functions, implementable via a variety of cryptographic constructions, in which Merkle trees plays vital role in constructing the composable attestation proof. Alternative constructions, including accumulator-based schemes and multi-signature approaches, are also explored, each presenting distinct trade-offs in performance, security, and functionality. Formal analysis demonstrates the adherence of these implementations to the fundamental properties . The framework's utility extends to applications such as secure AI model integrity verification , federated learning, and runtime trust assurance. The concept of attestation inclusion is introduced, permitting incremental integration of new components without necessitating full system re-attestation. This generalized approach reinforce trust in AI computation and broader distributed computing environments through cryptographically verifiable proof mechanisms, building upon foundational concepts of bootstrapping trust.

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