When Text Becomes Code: Securing LLM–Database Integrations
When you connect a large language model to your production data, you’re no longer just shipping code; you’re shipping conversations that can execute. And conversations are messy.
At a recent Quito Lambda community event, we walked through how prompt injection attacks can compromise LLM applications that generate SQL over live databases, and how to defend them with layered controls. This post translates that session into a written guide for engineers who are building these systems today, or are about to.
We’ll stay close to one concrete scenario: an LLM-powered SQL analyst over a Postgres database, using an open-source model accessed via API and a Streamlit frontend.
The Setup: An LLM as Your SQL Analyst
