Securing AI Systems: A Practical Guide to LLM Safety

LLM security is not the same as application security, and teams that treat it as such will encounter failure modes they did not design against. The threat model for an AI-powered system includes attack vectors that do not exist in traditional software: prompt injection, jailbreaking, indirect instruction hijacking through retrieved documents, and exfiltration through model outputs.

This guide covers the security posture every production AI system should have, organised by the OWASP LLM Top 10 framework with practical mitigations for each category.

Prompt Injection Is the SQL Injection of AI Systems

Prompt injection attacks occur when user-controlled input is incorporated into a prompt in a way that allows the attacker to override the system's intended instructions. If your system retrieves documents from external sources and includes their content in LLM context, every retrieved document is a potential injection vector.

"Assume all user input is adversarial. Assume all retrieved content is adversarial. Your prompt architecture should be robust to both — not just the first."

Data Exfiltration Through Model Outputs

LLMs can be manipulated into including sensitive information in their outputs in ways that are not obvious from the prompt. Overly permissive system prompts, broad tool access, and insufficient output filtering are the primary enablers. The mitigation is output filtering at the application layer, not just at the model level.

For systems that process sensitive data — PII, financial records, health information — implement a structured output validation layer that checks for sensitive data patterns before returning LLM responses to clients. This is not a substitute for proper input controls, but it is an important defence-in-depth layer.

Building a Secure AI Architecture

Principle of least privilege applies to AI agents as strongly as it does to service accounts. Grant tools only the specific permissions they need, with audit logging on every tool invocation. Implement rate limiting and anomaly detection on tool usage patterns — an agent that suddenly starts making 10x its normal number of external API calls may be executing an injected instruction.