AI security extends beyond the model itself. Every AI application relies on a complex supply chain of foundation models, third-party libraries, training datasets, APIs, and deployment pipelines. A compromise in any of these components can introduce hidden backdoors, manipulate outputs, expose sensitive data, or provide attackers with persistent access.
This infographic highlights five critical points where AI supply chain attacks can originate and outlines the warning signs security teams should monitor. From poisoned training data and vulnerable open-source dependencies to insecure APIs and compromised CI/CD pipelines, understanding these risks is essential for building resilient AI systems.
Whether you’re deploying generative AI, AI agents, or machine learning applications, securing the entire AI supply chain is key to reducing cyber risk and maintaining trust.
Explore the infographic to understand where AI backdoors can hide and how proactive governance, validation, and continuous monitoring can help strengthen your AI security posture.
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