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.

翻译：智能体系统正日益与地理信息系统（GIS）深度融合，其中多智能体协同能够实现复杂的对话与空间分析，但也带来了安全风险。本研究提出一个面向安全的框架，用于多智能体GIS系统的风险识别、评估与缓解，同时保持对更广泛智能体架构的适应性。我们在测试某商业地理空间合作方的智能体系统时，开发了一种基于状态机的模块化编排框架，将智能体行为抽象为可复用组件。我们采用红队测试框架评估系统鲁棒性，其中包含一个自适应的攻击者大语言模型（LLM），以及一个在多轮攻击中产出二元判定结果并附带支持性理由的确定性判官。我们进一步通过提示优化框架提升系统韧性，该框架将提示视为结构化签名并注入对抗性演示，从而在不降低任务性能的前提下实现系统化安全改进。