Risk-Aware LLM Agents for Geospatial Data Retrieval: Design and Preliminary Adversarial Evaluation
Quick Answer
The proposed LLM-driven framework efficiently retrieves remote sensing data via natural language queries, integrating Guardrail, General-QA, and Recommender-Analyst agents for robust API interactions.
Quick Take
Preliminary adversarial evaluations indicate that safety instructions enhance system resilience, though high-impact failures in API manipulation highlight the need for adaptive defenses.
Key Points
- Framework converts user intent into structured API calls for satellite imagery access.
- Integrates three agents: Guardrail, General-QA, and Recommender-Analyst for enhanced safety.
- Supports applications in environmental monitoring, disaster response, and climate analysis.
- Preliminary tests show prompt-level safety instructions improve robustness against adversarial queries.
- High-impact failures in API manipulation indicate a need for adaptive system-level defenses.
Paper Resources
Source Excerpt
arXiv:2606. 15077v1 Announce Type: new Abstract: We present an -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 reliable, semantically aligned interaction with external data services. …
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