用多角色协作自动验证大模型在关键系统中的可靠性。
DURA-CPS: A Multi-Role Orchestrator for Dependability Assurance in LLM-Enabled Cyber-Physical Systems
- 划分安全监控、攻击评估等角色，由代理在模拟环境协同工作
- 实测可发现漏洞、管理性能影响并支持自适应恢复策略
- 适合需要高可靠性的自动驾驶等安全关键系统开发者
网络物理系统（CPS）越来越多地依赖先进AI技术应用于关键场景。然而，传统验证与确认方法难以应对AI组件的不可预测性和动态性。本文提出DURA-CPS框架，通过多角色编排自动化实现对AI驱动的CPS的迭代保障流程。在模拟环境中，为专用代理分配特定角色（如安全监控、安全评估、故障注入和恢复规划），持续评估并优化AI行为以满足各类可靠性要求。通过一个基于AI规划器的自动驾驶车辆通过十字路口的案例研究，结果表明DURA-CPS能有效检测漏洞、管理性能影响，并支持自适应恢复策略，为安全与安全关键系统提供结构化且可扩展的严格V&V解决方案。
原文摘要 · Abstract (English)
Cyber-Physical Systems (CPS) increasingly depend on advanced AI techniques to operate in critical applications. However, traditional verification and validation methods often struggle to handle the unpredictable and dynamic nature of AI components. In this paper, we introduce DURA-CPS, a novel framework that employs multi-role orchestration to automate the iterative assurance process for AI-powered CPS. By assigning specialized roles (e.g., safety monitoring, security assessment, fault injection, and recovery planning) to dedicated agents within a simulated environment, DURA-CPS continuously evaluates and refines AI behavior against a range of dependability requirements. We demonstrate the framework through a case study involving an autonomous vehicle navigating an intersection with an AI-based planner. Our results show that DURA-CPS effectively detects vulnerabilities, manages performance impacts, and supports adaptive recovery strategies, thereby offering a structured and extensible solution for rigorous V&V in safety- and security-critical systems.
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