Securing the AI Supply Chain

This chapter explores the strategic and technical dimensions of securing the artificial intelligence (AI) supply chain, emphasizing how adversaries exploit trust relationships, inherited components, and opaque dependencies to embed persistent vulnerabilities in AI systems. Through real-world attack vectors such as poisoned datasets, manipulated annotations, compromised pretrained models, and tainted cloud-based infrastructure, the chapter demonstrates how attackers compromise model behavior long before inference occurs. Rather than breaching systems postdeployment, supply chain attackers subvert the foundational building blocks that developers assume to be safe. Readers are guided through detailed mechanisms of artifact contamination, checkpoint manipulation, and the exploitation of third-party libraries, revealing how subtle parameter perturbations and poisoned components can survive validation, fine-tuning, and even version control. The chapter breaks down how public model zoos, serialized checkpoint formats, and open-source frameworks are repeatedly used as adversarial delivery channels. From gradient manipulation to metadata spoofing, the content illustrates how attackers operate silently, embedding logic that only activates under specific conditions and often evades standard evaluation metrics. Security professionals will take away critical lessons on provenance tracking, validation workflows, and operational discipline in model deployment pipelines. The chapter provides a structured understanding of how adversarial logic propagates through AI ecosystems and what defenders must do to counter this risk. By reframing model development through the lens of supply chain risk, it equips readers with a forward-looking defense strategy tailored to the unique realities of machine learning systems.