How to Add Security Guardrails to Python LLM Apps Against Prompt Injection

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A technical guide published on DEV Community outlines key security boundaries in large language model (LLM) applications built with Python. The article identifies three entry points for untrusted text: user input, retrieved content from RAG pipelines, and model output that feeds into further processing. It focuses on defending against prompt injection attacks, where malicious text attempts to override a model's instructions, by keeping user input in clearly delimited data channels separate from system instructions. The guide also covers input validation, output validation, and PII redaction as part of a broader 'security-forward' approach to LLM app development. Code examples using Anthropic's API illustrate the difference between unsafe string concatenation and safer, delimiter-based prompt construction.

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