The widespread deployment of Deep Neural Networks and Large Language Models (LLMs) relies on a foundational assumption of isolation that this dissertation challenges. This work systematically deconstructs security assumptions around AI and modern cloud infrastructure through a taxonomy of interaction levels that ranges from physical memory co-location to remote service interfaces. While significant research has addressed individual attack surfaces in isolation, the security community lacks a unified framework for reasoning about how physical, architectural, and algorithmic vulnerabilities manifest across the modern AI stack. This dissertation addresses that gap by demonstrating practical attacks that exploit assumptions at each layer of abstraction.

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