{
  "id": 6512,
  "url": "https://arxiv.org/abs/2604.00387v2",
  "title": "RAGShield: Detecting Numerical Claim Manipulation in Government RAG Systems",
  "summary": "Retrieval-Augmented Generation (RAG) systems are deployed across federal agencies for citizen-facing tax guidance, benefits eligibility, and legal information, where a single incorrect number causes direct financial harm. This paper proves that all embedding-based RAG defenses share a fundamental blind spot: changing a tax deduction by $50,000 produces cosine similarity 0.9998, invisible to every known detection threshold. Across 174 manipulation pairs and two embedding models, the mean sensitiv",
  "authors": "KrishnaSaiReddy Patil",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": "us",
  "published_at": "2026-04-01T02:16:42.000Z",
  "fetched_at": "2026-07-14T16:32:33.100Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6512",
  "original_url": "https://arxiv.org/abs/2604.00387v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}