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arXiv · 2606.17092

Securing Multi-Agent GIS Systems: Risk Evaluation and Prompt Hardening Optimization

Kyle Gao·Pranavi Kotta·Linlin Xu·Jonathan Li·David A. Clausi
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Abstract

Agentic systems are increasingly integrated with geographic information systems (GIS), where multi-agent coordination enables complex conversational and spatial analysis but introduces security risks. This work presents a security-oriented framework for risk identification, evaluation, and mitigation in a multi-agent GIS system while maintaining adaptability to broader agentic architectures. We test the agentic system of a commercial geospatial partner while developing a modular state-machine-based orchestration framework that abstracts agent behavior into reusable components. We evaluate robustness using a red-teaming framework with an adaptive attacker LLM and a deterministic judge that produces binary outcomes with supporting rationales across multi-turn attacks. We further improve resilience with a prompt optimization framework that treats prompts as structured signatures and injects adversarial demonstrations, enabling systematic security improvements without degrading task performance.

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Kyle Gao, Pranavi Kotta, Linlin Xu, Jonathan Li, David A. Clausi. 2026-06-13. Securing Multi-Agent GIS Systems: Risk Evaluation and Prompt Hardening Optimization. https://doi.org/10.1109/lgrs.2026.3726972

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