Muhammad Zia

Lounge found me through my GitHub, so that's where I'll start.
A while back I was adding an AI assistant to a customer support tool. Routine work. I typed a slightly odd question and the model dumped part of its own system prompt into the chat.

I hadn't done anything clever. Which meant anyone could do it on purpose.
That afternoon turned into prompt-protection. It started as a prompt-injection filter. Version 3 is a tool-call guard for JavaScript agents. It tracks where every piece of data came from, and untrusted data never reaches a network, email, exec, file or payment call. It's listed in the official MCP registry.

Four smaller libraries sit around it, all on npm:
🛡️ mcp-policy-guard: decides which MCP tool calls are allowed, which need a human, and what a response may contain
🌊 streamguard-react: error boundaries for streaming LLM UIs that keep the partial tokens when a stream drops
📏 ragbench-lite: golden-question evals for RAG pipelines that fail CI when retrieval regresses
⚛️ react-crash-guard: React error boundaries that catch the async errors a normal boundary misses…