ABSTRACT:
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.
Key words and phrases: Artificial intelligence, vulnerability management, machine learning, sparsely labeled network, graph representation learning, self-supervised learning, open source software, computational design science, cybersecurity