Your AI Agent Will Be Prompt-Injected. Here's How to Defend It.

Someone will paste "ignore all previous instructions" into your AI agent. The question is whether your agent obeys. Prompt injection is the #1 vulnerability in the OWASP Top 10 for LLM Applications (2025). It happens when user input overrides your system instructions — causing your agent to leak data, execute unauthorized actions, or ignore its safety constraints entirely. The uncomfortable truth: there is no silver bullet. LLMs cannot reliably distinguish between instructions and data. But you can layer defenses that make exploitation expensive, detectable, and contained. Here are 4 patterns with working Python code. Pattern 1: Input Validation Before the LLM Sees It The first line of defense is never letting dangerous input reach your model. Most developers skip this step entirely — they pass raw user input straight into the prompt. The fix is a validation layer that runs before the LLM call. Pydantic makes this straightforward: import re from pydantic import BaseModel, field_validat