ASTRAL applies multimodal LLMs with prompt chaining and few-shot learning to synthesize CPS architectures from disparate sources, enabling adaptive threat identification and quantitative risk estimation, as supported by ablation studies and feedback from 14 cybersecurity practitioners.
Leveraging llms to automate software architecture design from informal specifications
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An enhanced LLM-assisted pipeline with refined prompting and multi-level staged representations improves consistency, scalability, and robustness when recovering hierarchical architectures from a real-world ROS 2 disassembly system.
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From Incomplete Architecture to Quantified Risk: Multimodal LLM-Driven Security Assessment for Cyber-Physical Systems
ASTRAL applies multimodal LLMs with prompt chaining and few-shot learning to synthesize CPS architectures from disparate sources, enabling adaptive threat identification and quantitative risk estimation, as supported by ablation studies and feedback from 14 cybersecurity practitioners.
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Towards LLM-Assisted Architecture Recovery for Real-World ROS~2 Systems: An Agent-Based Multi-Level Approach to Hierarchical Structural Architecture Reconstruction
An enhanced LLM-assisted pipeline with refined prompting and multi-level staged representations improves consistency, scalability, and robustness when recovering hierarchical architectures from a real-world ROS 2 disassembly system.