Protecting the Linked Artificial Intelligence Repositories on Open Source Software Platforms: A Graph Self-Supervised Learning Approach

• 成果类型: Article
• 署名作者: Lazarine, Ben Uribe; Samtani, Sagar; Zhu, Hongyi; Venkataraman, Ramesh
• 署名单位: Auburn University System; Auburn University; Indiana University System; Indiana University Bloomington; University of Texas System; University of Texas at San Antonio; Indiana University System; Indiana University Bloomington
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222; 1557-928X
• DOI: 10.1080/07421222.2026.2692274
• 发表日期: 2026-07-03
• 页码: 846-881
• 关键词: Artificial intelligence; vulnerability management; Machine Learning; sparsely labeled network; graph representation learning; self-supervised learning; open source software; computational design science; Cybersecurity; design science research; INFORMATION; FRAMEWORK

摘要

Artificial intelligence (AI) developers have leveraged open source software (OSS) to accelerate AI's progress. However, this has introduced security issues, including newly developed machine learning open-source software (MLOSS) repositories inheriting vulnerabilities from each other, typically lacking any explicit signal. In this study, we adopted the computational design science paradigm to design a novel MLOSS Link Prediction framework to map the spread of vulnerabilities across AI. We propose a Self-Supervised AI-Feature Aware Graph Attention Autoencoder (SSAIF-GATE) to learn from a sparsely labeled network, a novel AI-Feature Aware attention mechanism that captures shared AI terms, and a multilevel pretext task to leverage multiple components of a network's structure. SSAIF-GATE outperforms prevailing graph embedding methods with an area-under-the-curve of 94.8 percent and an average precision of 96.1 percent. SSAIF-GATE helps address extensive vulnerability spread among MLOSS and contributes design principles that can inform future information technology artifact design for broader domains including business intelligence and healthcare.