This morning, while working on an LLM integration in my own financial analysis tool, I encountered an unintended response. While expecting a simple data query, the model spilled out a text explaining my system configuration. At first, I thought it was a bug, but upon closer inspection, I realized it was a "prompt injection". Such attacks can pose serious security risks, especially in enterprise software and systems that process sensitive data.
As Large Language Models (LLMs) rapidly integrate into our lives, they bring security vulnerabilities along with them. Prompt injection is a type of attack that allows LLMs to take commands outside of expectations and perform malicious actions. In this post, drawing from my own experiences, I will explain in 5 steps how we can build more resilient systems against these threats. My goal is not just to present theoretical information, but to equip you with practical solutions directly from the field.
1. Input Sanitization & Validation
Every input coming to LLMs is a potential attack vector. Therefore, strictly controlling the input must be our first line of defense. We must determine what kind of inputs the model we use can work with and reject everything outside of these boundaries. This is of critical importance, especially in free-text inputs coming from users.
