Building AI agents is becoming easier.
Securing them is becoming much harder.
Most prompt injection discussions focus on user input, but production systems process much more than that. They consume retrieved documents, API responses, search results, tool outputs, emails, knowledge bases, and countless other external sources.
Every one of those can become an attack vector.
In this technical guide, I walk through a production-oriented Runtime Prompt Defense architecture using Lakera Guard as middleware before the LLM.
Topics include:
• Direct and indirect prompt injection
• Runtime validation for tool responses
• Output filtering
• Next.js Edge Runtime implementation
• Langfuse observability
• OWASP ASI 2026 mapping
• Multi-layer enterprise security architecture
• Comparison with other runtime defense platforms
The goal wasn't simply to explain prompt injection, but to show how security teams and AI engineers can build practical runtime defenses without introducing unacceptable latency or operational complexity.
I'd love feedback from developers already deploying AI agents in production. How are you validating external content today?
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