Open Source Summit North America 2025

The widespread adoption of Large Language Models (LLMs) like GPT-4, Claude, and Gemini has introduced unprecedented capabilities and equally unprecedented risks. Organizations are increasingly deploying LLMs to handle sensitive tasks, from processing medical records to analyzing financial documents. This talk examines the evolving landscape of LLM security and privacy, combining theoretical foundations with a walkthrough of example implementations.

Through real-world case studies of both attacks and defenses and practical implementation guidance using popular security tools, we'll explore critical vulnerabilities and proven defensive techniques. Special attention will be given to securing fine-tuned and domain-specific LLMs, with live examples using NVIDIA’s NeMo Guardrails, LangChain's security tools, and Microsoft's guidance library.