{
  "id": 1035,
  "url": "https://arxiv.org/abs/2606.15077v1",
  "title": "Risk-Aware LLM Agents for Geospatial Data Retrieval: Design and Preliminary Adversarial Evaluation",
  "summary": "We present an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries. The system converts user intent into structured API calls, enabling efficient access to satellite imagery and environmental datasets. The architecture integrates three agents: Guardrail for safety and policy enforcement, General-QA for intent interpretation, and Recommender-Analyst for schema-aware API call generation. This coordinated design ensures reliab",
  "authors": "Kyle Gao, Joel Cumming, Jonathan Li, Linlin Xu, David A. Clausi",
  "category": "research",
  "topics": "regulation,safety-alignment,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-13T03:15:53.000Z",
  "fetched_at": "2026-07-14T14:14:59.014Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1035",
  "original_url": "https://arxiv.org/abs/2606.15077v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}