Open, unclassified research on secure autonomy is constrained by limited access to operational platforms, contested communications infrastructure, and representative adversarial test conditions. This paper presents a threat-oriented digital twinning methodology for cybersecurity evaluation of learning-enabled autonomous platforms. The approach is instantiated as an open-source, modular twin of a representative autonomy stack with separated sensing, autonomy, and supervisory-control functions; confidence-gated multi-modal perception; explicit command and telemetry trust boundaries; and runtime hold-safe behavior. The contribution is methodological: a reproducible design pattern that translates threat analysis into observable, controllable tests for spoofing, replay, malformed-input injection, degraded sensing, and adversarial ML stress. Although the implemented proxy is ground based, the architecture is intentionally framed around stack elements shared with UAV and space systems, including constrained onboard compute, intermittent or high-latency links, probabilistic perception, and mission-critical recovery behavior. The result is an implementable research scaffold for dependable and secure autonomy studies across UAV and space domains.

翻译：开放、非保密的自主系统安全性研究受限于对运行平台、争议通信基础设施及代表性对抗测试条件的有限访问。本文提出一种面向威胁的数字孪生方法，用于具备学习能力的自主平台网络安全评估。该方法通过开源模块化孪生系统实现，该系统包含代表性自主技术栈，具有分离的感知、自主与监控控制功能；置信门控多模态感知；明确的命令与遥测信任边界；以及运行时安全保持行为。本研究的贡献在于方法论层面：一种可复现的设计模式，可将威胁分析转化为针对欺骗攻击、重放攻击、畸形输入注入、感知退化及对抗性机器学习压力测试的可观测、可控测试。尽管实现的代理系统基于地面平台，但架构设计明确围绕无人机与航天系统共享的技术栈要素展开，包括受限机载计算能力、间歇性或高延迟链路、概率性感知及关键任务恢复行为。最终成果为无人机与航天领域的可靠安全自主性研究提供了可实施的研究框架。