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SoK: How Frontier AI Reshapes System-Level Security Risk Dynamics in Critical Infrastructure

arXiv Security Archived Aug 06, 2026 ✓ Full text saved

arXiv:2608.04033v1 Announce Type: new Abstract: Frontier artificial intelligence (FAI), encompassing large-scale, general-purpose AI systems, including large language models, multimodal foundation models, and agentic systems, is increasingly integrated into critical infrastructure (CI). This challenges long-standing security assumptions of bounded behavior, segmented networks, component transparency, and human-paced decision-making. Existing AI-security literature typically organizes risks by at

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    Computer Science > Cryptography and Security [Submitted on 3 Aug 2026] SoK: How Frontier AI Reshapes System-Level Security Risk Dynamics in Critical Infrastructure Chandra Thapa, Mohan Baruwal Chhetri, Marthie Grobler, Shahroz Tariq, Tooba Aamir Frontier artificial intelligence (FAI), encompassing large-scale, general-purpose AI systems, including large language models, multimodal foundation models, and agentic systems, is increasingly integrated into critical infrastructure (CI). This challenges long-standing security assumptions of bounded behavior, segmented networks, component transparency, and human-paced decision-making. Existing AI-security literature typically organizes risks by attack type, lifecycle stage, or asset class, but fails to capture the system-level dynamics, such as how risk emerges, spreads, and is controlled, through which FAI reshapes CI security outcomes. This Systematization of Knowledge (SoK) introduces a five-dimensional risk-dynamics framework that characterizes how FAI reconfigures CI security across the lifecycle: (i) Capability Emergence through new FAI-enabled attack and defense capabilities, (ii) Infiltration Pathways through data, models and AI supply chains, (iii) Cross-System Propagation across interconnected infrastructures and dependencies, (iv) degradation of effective technical and human Control Authority, and (v) strain on institutional Response Capacity under operational pressure. Rather than enumerating threats, the framework identifies recurring mechanisms that jointly determine system-level risk. We further identify a structural mismatch between academic AI-security research and CI operational constraints, and derive a deployment-oriented research agenda grounded in system-level assurance criteria. Collectively, this work shifts attention from model-centric robustness to lifecycle-structured, system-level assurance in interconnected CI environments. Comments: 19 pages Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.04033 [cs.CR]   (or arXiv:2608.04033v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.04033 Focus to learn more Submission history From: Chandra Thapa [view email] [v1] Mon, 3 Aug 2026 01:30:35 UTC (348 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Aug 06, 2026
    Archived
    Aug 06, 2026
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