Dependence-Aware Risk Assessment and Optimal Design of AI-Agent Safeguard Systems

arXiv cs.AINovember 3, 2026en

arXiv:2611.00386v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on safeguards to prevent unauthorized actions while preserving legitimate service. However, existing risk assessments often focus on individual safeguard performance while overlooking dependence among safeguard decisions. This paper develops a dependence-aware framework for risk assessment and optimal design of multi-component AI safeguard systems, jointly selecting configurations and operating policies for sequential layered screening and selective verification. We establish stochastic comparisons of layered-screening error probabilities and invocation costs under lower-orthant ordering. For

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