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Human-Certified Module Repositories for the AI Age

Szilárd Enyedi

TL;DR

Human-Certified Module Repositories are introduced in this work as a new architectural model for constructing trustworthy software in the era of AI-assisted development, positioning HCMRs as a foundational substrate for reliable and auditable AI-constructed software systems.

Abstract

Human-Certified Module Repositories (HCMRs) are introduced in this work as a new architectural model for constructing trustworthy software in the era of AI-assisted development. As large language models increasingly participate in code generation, configuration synthesis, and multi-component integration, the reliability of AI-assembled systems will depend critically on the trustworthiness of the building blocks they use. Today's software supply-chain incidents and modular development ecosystems highlight the risks of relying on components with unclear provenance, insufficient review, or unpredictable composition behavior. We argue that future AI-driven development workflows require repositories of reusable modules that are curated, security-reviewed, provenance-rich, and equipped with explicit interface contracts. To this end, we propose HCMRs, a framework that blends human oversight with automated analysis to certify modules and support safe, predictable assembly by both humans and AI agents. We present a reference architecture for HCMRs, outline a certification and provenance workflow, analyze threat surfaces relevant to modular ecosystems, and extract lessons from recent failures. We further discuss implications for governance, scalability, and AI accountability, positioning HCMRs as a foundational substrate for reliable and auditable AI-constructed software systems.

Human-Certified Module Repositories for the AI Age

TL;DR

Human-Certified Module Repositories are introduced in this work as a new architectural model for constructing trustworthy software in the era of AI-assisted development, positioning HCMRs as a foundational substrate for reliable and auditable AI-constructed software systems.

Abstract

Human-Certified Module Repositories (HCMRs) are introduced in this work as a new architectural model for constructing trustworthy software in the era of AI-assisted development. As large language models increasingly participate in code generation, configuration synthesis, and multi-component integration, the reliability of AI-assembled systems will depend critically on the trustworthiness of the building blocks they use. Today's software supply-chain incidents and modular development ecosystems highlight the risks of relying on components with unclear provenance, insufficient review, or unpredictable composition behavior. We argue that future AI-driven development workflows require repositories of reusable modules that are curated, security-reviewed, provenance-rich, and equipped with explicit interface contracts. To this end, we propose HCMRs, a framework that blends human oversight with automated analysis to certify modules and support safe, predictable assembly by both humans and AI agents. We present a reference architecture for HCMRs, outline a certification and provenance workflow, analyze threat surfaces relevant to modular ecosystems, and extract lessons from recent failures. We further discuss implications for governance, scalability, and AI accountability, positioning HCMRs as a foundational substrate for reliable and auditable AI-constructed software systems.
Paper Structure (34 sections, 3 figures, 2 tables)

This paper contains 34 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Related Foundations and Ecosystems
  4. Formal Verification Ecosystems
  5. Supply‑Chain Attack Literature
  6. Provenance and Signing Frameworks
  7. Trusted Data Repositories
  8. Trigger–Action and Flow‑Based Programming Ecosystems
  9. Cloud‑Native Module Libraries
  10. Problem Statement and Core Requirements
  11. Human-Certified Module Repositories: Architecture and Workflow
  12. Design Principles
  13. Certification Pipeline
  14. Metadata Model
  15. AI-Assisted Assembly Pipeline
  16. ...and 19 more sections

Figures (3)

  • Figure 1: SLSA-inspired provenance generation and attestation pipeline.
  • Figure 2: HCMR certification pipeline, showing the four stages of intake, security review, behavioral validation, and final certification.
  • Figure 3: Identity-based signing and transparency logging workflow inspired by Sigstore.