Designing Secure Composite AI Systems: Cross-Domain Holistic Threat Model and Mitigation Framework

Engineering notes

Key topics: autonomous driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Composite artificial intelligence (AI) systems are increasingly deployed in mission-critical environments, such as defence, aerospace, industrial control systems, and critical infrastructure, where they enable adaptive, autonomous, and real-time decision-making. However, the growing complexity of these systems introduces multilayered security risks that extend far beyond the assumptions of traditional, component-centric security models. In this work, we introduce a structured taxonomy that decomposes composite AI systems into five tightly interconnected layers: core AI and machine-learning (ML) components, integration and orchestration mechanisms, data flows and shared computational resources, cross-layer system interactions and emergent vulnerabilities, and legacy or deterministic software modules that coexist with AI. Leveraging this taxonomy, we propose a holistic, cross-domain threat modelling approach to systematically identify threats, architectural weaknesses, and design-level security flaws across the entire system lifecycle. Finally, we outline mitigation strategies and architectural best practices aimed at building secure, resilient, and trustworthy composite AI systems capable of operating safely under adversarial conditions.

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