SafeAgent: A Runtime Protection Architecture for Agentic Systems
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-awa...
| Authors | Hailin Liu, Eugene Ilyushin, Jie Ni et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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