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

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.

• 저널Journal of Management Information Systems · 43(3) · 846–881
• 토픽Advanced Graph Neural Networks · Artificial Intelligence
• DOI10.1080/07421222.2026.2692274
• 저자Ben Uribe Lazarine, Sagar Samtani, Hongyi Zhu, Ramesh Venkataraman