{
  "id": 5653,
  "url": "https://arxiv.org/abs/2604.17562v1",
  "title": "SafeAgent: A Runtime Protection Architecture for Agentic Systems",
  "summary": "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",
  "authors": "Hailin Liu, Eugene Ilyushin, Jie Ni, Min Zhu",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-19T18:02:21.000Z",
  "fetched_at": "2026-07-14T16:31:53.169Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5653",
  "original_url": "https://arxiv.org/abs/2604.17562v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}