AI Container Security Begins Inside the Workload

The report summarizes insights from Aqua’s Nautilus honeypots showing that many AI security risks occur inside containers running AI workloads, not just at the perimeter. It argues that vulnerabilities, misconfigurations, poisoned components, and prompt injections can lead to privilege escalation or lateral movement, and recommends container-level visibility and runtime protection (via Aqua Secure AI) to detect and prevent malicious behavior without changing models or code.