SafeAgent Architecture Proposes Runtime Protection for LLM Agents Against Prompt-Injection Attacks

A recent study presents SafeAgent, a security framework aimed at safeguarding large language model agents against prompt-injection threats. Such vulnerabilities can propagate through multi-step processes, tool interactions, and ongoing context, making basic input-output filtering insufficient. This architecture treats agent safety as a stateful decision-making challenge along changing interaction paths. It distinguishes between execution governance and semantic risk assessment through two integrated components: a runtime controller that manages actions within the agent loop and a context-aware decision core that functions over a persistent session state. This core is defined as a context-aware advanced machine intelligence and is realized through operators for risk encoding, utility-cost assessment, consequence modeling, policy arbitration, and state synchronization. Validation experiments utilized the Agent Security Bench and InjecAgent datasets. The study is cataloged as arXiv:2604.17562v1.