What is LLM runtime security and how does it protect AI agents?

LLM runtime security is the layer that monitors and controls an AI agent while it is live: as it processes prompts, calls tools, accesses resources, and returns responses. It exists to catch the attacks that only appear in production, which is where most agentic risk actually lives.

The distinction from pre-deployment testing is the whole point. Static testing finds the issues you can anticipate before shipping. Runtime security catches the ones that did not exist until a user, a tool response, or a retrieved document introduced them. A common example: an indirect prompt injection arriving inside content the agent retrieves at request time, instructing it to exfiltrate data through a tool call. No pre-launch scan saw that input, because it did not exist at launch.

In practice, runtime security maps to several categories in the OWASP LLM Top 10, including prompt injection, sensitive information disclosure, and excessive agency. Effective runtime protection should:

• Inspect prompts and the downstream tool calls they trigger together, not in isolation.

• Block unauthorized tool executions and resource access before they run.

• Detect sensitive data leaving through outputs or tool interactions.

• Hold a policy line consistently across cloud, hybrid, and on-prem deployments.

Runtime security is a layer, not a replacement for testing or design-time controls. The strongest programs pair it with continuous adversarial testing so issues are found before deployment and contained during it.

Akto's Agentic Runtime Protection provides this enforcement layer for AI agents, MCP servers, and GenAI applications. Security teams define guardrails that allow or deny prompts, tool executions, resource access, and data transfers based on organizational policy, giving regulated environments a final control point between agents and critical systems.