Episode 16 — Agents as an Attack Surface
This episode introduces AI agents as a new and growing attack surface, highlighting how their autonomy and tool integration create unique risks. Agents differ from single-response models by persisting through plan-and-act loops, chaining multiple steps, and invoking external tools or APIs. For certification purposes, learners must understand that these design features expand the system boundary, exposing new trust assumptions and vulnerabilities. Risks include prompt injection, privilege escalation, excessive resource consumption, and data exfiltration when agents interact with connected services. Recognizing how agents differ from classical models allows exam candidates to frame their answers within the context of evolving adversarial surfaces.
The applied perspective covers scenarios such as agents issuing repeated API calls without oversight, retrieving poisoned content that alters their instructions, or escalating access through poorly scoped credentials. Best practices include sandboxing, rate limiting, least-privilege permissioning, and continuous monitoring of agent actions. Troubleshooting considerations emphasize challenges of detecting malicious behavior when tasks are multi-step and distributed across external systems. For certification readiness, learners must be able to describe both attack patterns and defensive strategies, showing an understanding of how agents multiply complexity in AI security environments. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
