Toward a Unified Security Systems Theory for Autonomous AI Systems

Abstract

Security in autonomous AI systems is not a property of individual components such as identity verification, static policy configurations, or model-level safeguards. Instead, it is a dynamic system property emerging from three interconnected dimensions: intent, time, and interaction. Intent captures the alignment between an operator’s goals and an agent’s behavior; time reflects the evolution of security states under adversarial pressure; and interaction describes how trust relationships between agents can propagate misalignment. This paper synthesizes the findings of the five-paper AI Agent Security Series into a unified, formal, and falsifiable theory of autonomous agent security. Current AI security approaches focus on component-level controls, including authentication, policy enforcement, and output filtering. However, results from Papers 1–5 show that these mechanisms are individually insufficient and collectively vulnerable to clean attacks semantically valid, policy-compliant adversarial inputs that redirect agent behavior while evading existing detection methods. We argue that such failures are structurally inevitable under component-centric security models. To address this gap, we introduce the Unified Security Function, Φ_security(φ, t, G), a scalar measure in the range [0,1] that combines IntentAlign, TemporalStability, and NetworkIntegrity into a single dynamic assessment of security health. Using this framework, we establish three meta-theorems: the Component Insufficiency Theorem, the Dynamic Necessity Theorem, and the Interaction Irreducibility Theorem. Evaluation on the complete AegisBench benchmark suite (1,560 sessions across Papers 1–5) shows that Φ_security predicts attack success with an AUC of 0.943, outperforming all individual metrics. By defining autonomous agent security as a distinct scientific object, this work provides a foundational framework for future research on secure and adaptive AI systems.