It’s no secret how artificial intelligence (AI)-powered tools and systems, which promise users exponential productivity, growth, and efficiency, have quickly become integral in organizations' processes and workflows. AI — specifically generative AI (GenAI) and large language models (LLMs) — is now a foundational element of the software development process, allowing developers to build and scale software at a velocity that would have been impossible a mere decade ago. These technologies have permeated the software development lifecycle (SDLC), from ideation to maintaining software, ensuring that concepts are translated swiftly into code.

Update Oct 22 2026: New sections were added for “Observed Threat Activity and Indicators” and “Mitigations and Recommendations.”

Executive Summary: Bypassing Boundaries in Enterprise AI Infrastructure

The massive global adoption of artificial intelligence (AI) and large language models (LLMs) has fundamentally rewritten the enterprise threat landscape. Modern high-compute bare metal clusters, cloud nodes, and developer workstations now routinely host dense data science stacks running frameworks such as TensorRT LLM, PyTorch, and deep CUDA execution layers. To process complex matrix calculations efficiently, low-privilege user mode tasks require direct pipelines straight to the underlying graphics acceleration hardware.

Artificial intelligence (AI) is changing the shape of the application attack surface. A traditional application assessment usually starts with familiar questions, such as: