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arXiv · 2604.17562

SafeAgent: A Runtime Protection Architecture for Agentic Systems

Hailin Liu·Eugene Ilyushin·Jie Ni·Min Zhu
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Abstract

Large language model (LLM) agents are vulnerable to prompt-injection attacks that propagate through multi-step workflows, tool interactions, and persistent context, making input-output filtering alone insufficient for reliable protection. This paper presents SafeAgent, a runtime security architecture that treats agent safety as a stateful decision problem over evolving interaction trajectories. The proposed design separates execution governance from semantic risk reasoning through two coordinated components: a runtime controller that mediates actions around the agent loop and a context-aware decision core that operates over persistent session state. The core is formalized as a context-aware advanced machine intelligence and instantiated through operators for risk encoding, utility-cost evaluation, consequence modeling, policy arbitration, and state synchronization. Experiments on Agent Security Bench (ASB) and InjecAgent show that SafeAgent consistently improves robustness over baseline and text-level guardrail methods while maintaining competitive benign-task performance. Ablation studies further show that recovery confidence and policy weighting determine distinct safety-utility operating points.

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Hailin Liu, Eugene Ilyushin, Jie Ni, Min Zhu. 2026-04-19. SafeAgent: A Runtime Protection Architecture for Agentic Systems. https://arxiv.org/abs/2604.17562

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