Securing Agentic AI With PyTorch: Threat Modeling & LLM Red Teaming in Practice - Valeri Milke

Securing Agentic AI With PyTorch: Threat Modeling & LLM Red Teaming in Practice - Valeri Milke, VamiSec GmbH Agentic AI systems built with PyTorch introduce a new security paradigm: autonomous decision-making, tool usage, memory, and multi-step reasoning significantly expand the attack surface beyond traditional ML pipelines. This session presents a practical, security-first approach to building and testing agentic AI systems using PyTorch, combining AI threat modeling and hands-on LLM security testing. We introduce MAESTRO-based AI Threat Modeling to systematically identify risks across prompts, tools, memory, orchestration and model interactions. Building on this foundation, we demonstrate how the OWASP LLM Top 10 and the OWASP LLM Testing Guide can be applied to real PyTorch-based agent architectures. The session includes a live demo of a prompt injection attack against an agentic workflow, showing how task delegation and tool invocation can be abused — and how developers can detect, mitigate and test these risks early in the AI development lifecycle. Attendees will leave with concrete techniques to integrate AI security testing and threat modeling into PyTorch-based systems, bridging research, engineering and real-world AI risk.