LLM-powered agents are rapidly moving from passive assistants to systems that can execute real commands modifying infrastructure, querying systems, and even remediating failures. This introduces a critical risk: how do we safely execute AI-generated actions without exposing the host system to prompt injection, hallucinations, or malicious inputs?

This talk presents a production-ready architecture that uses microVM-based sandboxing to isolate LLM tool execution. By leveraging lightweight virtualization such as Firecracker and Kata Containers, each AI-generated command is executed inside an ephemeral, disposable virtual environment, ensuring strong isolation with minimal overhead.

We demonstrate how LLM served via vLLM or running locally with llama.cpp can safely interact with system tools through a controlled execution pipeline. The session includes a live demo where prompt injection attempts are neutralized by sandbox policies, proving that AI can act autonomously without compromising security.