If you are moving AI agents from a toy script into production with enterprise clients, you quickly encounter a harsh reality: Traditional cybersecurity firewalls know nothing about probabilistic LLMs.
When your agent has access to tools (like SQL databases, web scrapers, shell commands, or Model Context Protocol servers), indirect prompt injection is not just an annoying glitch — it is a severe Remote Code Execution (RCE) and data exfiltration threat surface.
In this guide, we break down three production-proven defenses you can implement today to harden your AI agents against OWASP LLM Top 10 vulnerabilities.
Most agent developers concatenate user input or web crawl data directly into their prompts like this:
python
prompt = f"Summarize this customer ticket: {untrusted_customer_input}"
source & further reading
dev.to — original article
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