Side-Channel Attacks Bypass Protection in 3D Printers

Computer Science > Cryptography and Security [Submitted on 11 Jun 2026] Side-Channel Attacks Bypass Protection in 3D Printers Eric Yocam, Varghese Vaidyan, Micah Flack, Gurcan Comert, Judith L. Mwakalonge Active Motor Noise Cancellation (AMNC) ships in commercial fused deposition modeling (FDM) 3D printers as a hardware countermeasure against acoustic side-channel attacks that target intellectual property (IP). We present the first empirical evaluation of a deployed AMNC countermeasure, using a public dataset of synchronized acoustic and vibration recordings from two AMNC-equipped Bambu Lab printers across 12 object classes. AMNC fully neutralizes the acoustic channel: classification accuracy is indistinguishable from the 8.33% random baseline. The vibration channel, which AMNC does not target, still leaks. With summary statistics the leak is coarse and amplitude-driven (vibration accuracy approximately 31% pooled, 36-47% within-printer), while the waveform shape carries essentially nothing (frequency-only features at chance). A full-sequence temporal model that ingests the ordered evolution of the print raises accuracy to approximately 61%, and an order-shuffling control (approximately 33%) shows that a substantial component is genuinely sequential and tied to print progression. The leak is device-specific: a classifier trained on one printer transfers near chance to the other. We conclude that AMNC is an acoustic-only defense: vibration remains a partial, geometry-correlated side channel it does not address, but one that does not, on this dataset, support full geometric reconstruction; reconstruction-grade attacks would require the magnetic or power channels AMNC also leaves untouched. We release all code. Comments: 11 pages, 6 figures, 4 tables Subjects: Cryptography and Security (cs.CR); Emerging Technologies (cs.ET); Machine Learning (cs.LG) MSC classes: 68M25 ACM classes: C.3; K.6.5 Cite as: arXiv:2606.13952 [cs.CR]   (or arXiv:2606.13952v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.13952 Focus to learn more Submission history From: Eric Yocam [view email] [v1] Thu, 11 Jun 2026 22:32:06 UTC (570 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.ET cs.LG 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?)