Secrets Management for LLM Tools: Don’t Let Your OpenAI Keys End Up on GitHub 🚨

"A practical guide to securing LLM API keys, embeddings, vector TL;DR : If you're building with LLMs and you're not treating secrets as first-class infrastructure, you're already at risk. Every week, we see: OpenAI keys pushed to GitHub API keys logged in CloudWatch Secrets hardcoded in Streamlit demos that later go to production LLM systems multiply secrets quickly. If you don’t design for this early, things get messy fast. This is a production-ready blueprint for securing LLM systems properly. The Problem: LLM Secrets Multiply Fast 🐰 One LLM integration turns into dozens of credentials: 1 LLM API key (OpenAI / Anthropic) → 3 embedding endpoints → 5 vector store connections (Pinecone / Weaviate) → 2 RAG databases → 10 external tools (SerpAPI, Wolfram, etc.) → 50 microservices = 70+ secrets The bigger your AI system gets, the larger your attack surface becomes. 1️⃣ Never Hardcode Secrets ❌ Wrong (guaranteed leak eventually) # NEVER DO THIS from openai import OpenAI client = OpenAI ( ap