Secure LLM Applications for Enterprise

Segregate untrusted input and output layers by establishing strict boundaries. This minimizes the risk of malicious data flowing through the system, and prevents the interaction of unauthorized components with sensitive modules.

Only allow explicitly necessary external plugin or API calls. Failure to restrict their access increases the attack surface of an LLM app, so every plugin integration needs strict authentication and authorization protocols. For example, in the case of multiple plugins in series, one plugin’s output should not become another plugin’s input without explicit permission.

Without proper Input sanitization, prompt injections can enter the model, manipulate inputs and alter LLM outputs. Strict validation techniques are necessary to protect the integrity of the application from compromised incoming data.

Set a threshold for query frequency to prevent abuse. Rate limiting protects against denial-of-service attacks and can control cost and resource consumption by limiting user input per time period.

LLMs can generate unpredictable output. This is part of their appeal and their potential, but it also means that outputs should be handled with caution, especially in high-stakes environments. Always verify and filter LLM-generated data to avoid the pitfall of overreliance.

RAG enhances LLM responses by combining them with real-time data retrieval systems. A RAG architecture can help to keep LLM responses both relevant and accurate.

These vulnerability occurs when malicious users craft inputs to manipulate the AI into generating incorrect or harmful outputs, in both direct and indirect ways.