AI Agent Security Audit Checklist: 8 Critical Tests for Production Deployments
AI agents are no longer experimental. In 2026, enterprises are deploying LLM-powered agents that read databases, execute code, send emails, and control production infrastructure. The question is no longer "should we use AI agents?" but "how do we secure them in production?"
This article is the fourth in our AI Runtime Security series. We've covered the macro landscape, MCP penetration testing methodology, and why runtime call verification is the missing layer. Here, we distill that experience into a practical, actionable checklist — 8 tests every security team should run before putting AI agents into production.
Why this matters: We've audited 10+ AI agent frameworks using the Correctover CCS scanner, producing over 1,730 verified findings across 12 codebases. Of those, 87 are confirmed production vulnerabilities — real bugs in shipping code, not theoretical attack surfaces. Every item on this checklist is grounded in actual vulnerabilities we've found, reported, and in many cases had patched.
Checklist Overview
