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Spying Across Chiplets: Side-Channel Attacks in 2.5/3D Integrated Systems

arXiv Security Archived May 11, 2026 ✓ Full text saved

arXiv:2605.07486v1 Announce Type: new Abstract: Advanced packaging and chiplet-based integration are increasingly adopted to build complex heterogeneous systems beyond the limits of monolithic scaling. While these architectures offer major benefits in terms of modularity, yield, and performance, they also introduce new physical attack surfaces. In this paper, we show that side-channel attacks can be mounted across chiplets within the same package or stack. Our key idea is that a communication-or

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    Computer Science > Cryptography and Security [Submitted on 8 May 2026] Spying Across Chiplets: Side-Channel Attacks in 2.5/3D Integrated Systems Giorgio Di Natale, Christelle Rabache, Pierre-Louis Hellier, Florence Podevin, Sylvain Bourdel, Romain Siragusa, Paolo Maistri Advanced packaging and chiplet-based integration are increasingly adopted to build complex heterogeneous systems beyond the limits of monolithic scaling. While these architectures offer major benefits in terms of modularity, yield, and performance, they also introduce new physical attack surfaces. In this paper, we show that side-channel attacks can be mounted across chiplets within the same package or stack. Our key idea is that a communication-oriented chiplet, originally intended to interact with the external environment through an antenna, an RFID-like element, or another contactless coupling structure, can be repurposed as an internal observation platform. We formalize this threat through a realistic adversary model, describe the corresponding attack principle, and experimentally assess its feasibility. The obtained results demonstrate that signals captured through such a communication-oriented interface can reveal information correlated with the activity of a neighboring victim chiplet. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2605.07486 [cs.CR]   (or arXiv:2605.07486v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.07486 Focus to learn more Submission history From: Giorgio Di Natale [view email] [v1] Fri, 8 May 2026 09:27:24 UTC (5,514 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 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
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    ◬ AI & Machine Learning
    Published
    May 11, 2026
    Archived
    May 11, 2026
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