When the Agent Becomes the Kernel: A Systematization of Security on the Path to AI-Native Operating Systems

The paper discusses how large language model agents now act as privileged principals with kernel‑grade authority, yet lack the trusted mediation traditionally required for operating‑system security. It introduces a taxonomy that distinguishes between provenance‑based deterministic checks and content‑semantic checks, identifying a central mediation gap in distinguishing data from instruction and authorized from unauthorized actions. The authors argue that this gap creates an irreducible risk of undetected attacks whenever inputs and actions are not pre‑enumerated, and they propose defenses across runtime monitoring, architectural separation, and authorization while critiquing current evaluation practices. They extend the analysis to AI‑native operating systems where the model itself serves as the arbitration core, outlining design constraints, challenges, and a research agenda.