Agent Security is a Systems Problem: Google/UCSD/Meta Paper Argues AI Models Must Be Treated as Untrusted Components

arXiv·medium signal

A position paper from Google, UCSD, Meta, Gray Swan AI, Cornell, and others (arXiv:2605.18991, May 18) argues that agent security must be approached as a systems problem where the AI model is treated as an untrusted component and security invariants are enforced at the system level. The paper contends that efforts to increase model robustness alone are insufficient and must be complemented with systems security techniques — zero trust, capability-based access, and invariant enforcement. This reframes the entire agent security conversation: stop trying to make the model trustworthy, start building systems that work even when the model is compromised.