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 coordinate
Record details
Published: 19 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (19 April 2026), “SafeAgent: A Runtime Protection Architecture for Agentic Systems,” evidence record 5653, https://ethics.ai/record/5653 (originally published by arXiv).
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