How to Build a Secure Local AI Agent Using SGLang and Olares on Open-Source Stack

Running large language models on-premise offers data sovereignty, but local deployment does not automatically guarantee security, according to a technical analysis published on DEV Community. Poorly configured local LLM stacks can expose infrastructure to threats such as prompt injection, tool misuse, and adversarial inference attacks. The article examines SGLang, an open-source LLM serving engine developed by Princeton University, and Olares, a local AI orchestration framework, as building blocks for a hardened deployment. SGLang's structured output enforcement and schema validation are highlighted as key controls that reduce prompt injection risks, while its RadixAttention caching feature is flagged as a potential source of sensitive context leakage. The piece draws on real-world penetration testing lessons to argue that the trust boundary in local AI systems has shifted from the network perimeter to the prompt interface itself.