{
  "id": 6573,
  "url": "https://arxiv.org/abs/2603.29062v1",
  "title": "CivicShield: A Cross-Domain Defense-in-Depth Framework for Securing Government-Facing AI Chatbots Against Multi-Turn Adversarial Attacks",
  "summary": "LLM-based chatbots in government services face critical security gaps. Multi-turn adversarial attacks achieve over 90% success against current defenses, and single-layer guardrails are bypassed with similar rates. We present CivicShield, a cross-domain defense-in-depth framework for government-facing AI chatbots. Drawing on network security, formal verification, biological immune systems, aviation safety, and zero-trust cryptography, CivicShield introduces seven defense layers: (1) zero-trust fo",
  "authors": "KrishnaSaiReddy Patil",
  "category": "research",
  "topics": "military-security,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-30T22:58:04.000Z",
  "fetched_at": "2026-07-14T16:32:37.307Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6573",
  "original_url": "https://arxiv.org/abs/2603.29062v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}