Building a Secure Local AI Agent on Open-Source Infrastructure: Lessons from SGLang, Olares, and Real-World Audits

Short summary

A technical deep-dive into building secure local AI agent architectures using SGLang and Olares. Covers threat models unique to LLMs (prompt injection, tool misuse, data leakage), and provides production-hardened deployment patterns with code examples for structured output enforcement. Emphasizes that 'local' does not equal 'secure' and the trust boundary has shifted to the prompt interface.

• •Defines LLM-specific threat model: prompt injection, tool exploitation, context leakage, model poisoning
• •Analyzes SGLang security features including structured output enforcement and RadixAttention caching risks
• •Introduces Olares for sandboxed tool execution and local AI orchestration security policies

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