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arxiv: 2607.00186 · v1 · pith:WFQOTGNDnew · submitted 2026-06-30 · 💻 cs.CR

A Non-Line-of-Sight, Multi-Modality-based Side-Channel IP Theft Attack on Additive Manufacturing Using Dual Smartphones

Amirhossein Jamarani , Diba Afroze , Yazhou Tu , Mark Yampolskiy , Xiali Hei This is my paper

Pith reviewed 2026-07-02 18:39 UTC · model grok-4.3

classification 💻 cs.CR
keywords side-channel attackadditive manufacturing3D printingIP theftG-code reconstructionacoustic emissionsmagnetic emissionssmartphone sensors
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The pith

Dual smartphones reconstruct 3D printer G-code commands from acoustic and magnetic emissions at 60 cm distance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that two ordinary smartphones placed 60 cm away can capture acoustic and magnetic signals from a 3D printer even without line of sight. These signals are processed to recover the exact sequence of G-code commands that define the printed object, reaching 98.89 percent accuracy at the individual command level. The same approach transfers to a second printer in a different setting. Because G-code directly encodes the intellectual property of the manufactured part, the result demonstrates a practical route for unauthorized extraction of design data using only consumer devices.

Core claim

The attack uses dual smartphones' internal sensors to collect acoustic and magnetic emissions from a 3D printer at 60 cm in non-line-of-sight setup. It reconstructs the G-code commands of the final objects at 98.89% command-level reconstruction accuracy and demonstrates transferability to another printer in a different environment.

What carries the argument

Multi-modality side-channel using smartphone acoustic and magnetic sensors to reconstruct G-code sequences.

If this is right

  • G-code reconstruction at 98.89 percent accuracy directly exposes the intellectual property of printed objects.
  • The attack succeeds at 60 cm distance without requiring line of sight or specialized hardware.
  • Transferability across printers and environments indicates the method is not limited to one specific machine or location.
  • The results establish that consumer smartphones alone are sufficient to perform this side-channel extraction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar emissions from other computer-controlled manufacturing tools could be targeted with the same dual-sensor approach.
  • Defenses would need to address both acoustic and magnetic leakage simultaneously rather than one channel alone.
  • Extending the attack to additional sensor modalities on the same phones might further raise reconstruction rates.

Load-bearing premise

The acoustic and magnetic emissions captured by smartphone sensors at 60 cm contain sufficient unique and consistent information to enable high-accuracy reconstruction of arbitrary G-code sequences across different printers and environments.

What would settle it

Reconstruction accuracy falling below 80 percent when the same method is applied to a third printer model or a substantially altered room layout would show the claimed transferability does not hold.

Figures

Figures reproduced from arXiv: 2607.00186 by Amirhossein Jamarani, Diba Afroze, Mark Yampolskiy, Xiali Hei, Yazhou Tu.

Figure 1
Figure 1. Figure 1: End-to-end attack workflow showing how acoustic and magnetic [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: The attacker does not modify the printer’s hardware [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Experimental setup; the smartphones are hiddenly positioned on the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Angular comparison of dual-smartphone sensing configurations for [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Magnetic field measurements recorded by two smartphones during [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The red rectangles show the differences in the printer’s command [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of the original 3D model and the reconstructed shape [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Two 3D printers working at the same time for data interference [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Attack on the 3D printer in a new environment with a different 3D [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Acoustic emission signals captured during 3D printing (only moving on X-axis) with two different types of machines (LulzBot vs Creality). The [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: (a) Mean reconstruction accuracy decreases as the sensor distance [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
read the original abstract

Additive Manufacturing (AM) has revolutionized major sectors, including aerospace, automotive, and healthcare, by enabling adjustable production. As the usage of AM increases, so does the risk of Intellectual Property (IP) leakage during the printing process due to unintended side-channel emissions. Current studies and attack scenarios on 3D printers face three challenges: low success and accuracy rates in final G-code reconstruction, limited distance range for attacking the 3D printer's IP, and reliance on specialized, overt data-collection tools. This paper presents a side-channel attack that addresses the noted limitations by using two smartphones' internal sensors. We position the smartphones 60 cm away in a non-line-of-sight setup to collect the 3D printer's acoustic and magnetic emissions. Our attack successfully reconstructs the G-code commands of the final objects at a rate of 98.89% on command-level reconstruction accuracy. Additionally, we evaluate the transferability of our attack strategy by applying it to another 3D printer in a different environment. Our proven unauthorized access to the reconstructed G-code and thus to the IP of the AM system indicates the security weaknesses in 3D printing, highlighting the need for mitigating side-channel attacks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript presents a side-channel attack on additive manufacturing that uses the built-in acoustic and magnetic sensors of two smartphones placed 60 cm away in a non-line-of-sight configuration to capture emissions from a 3D printer. It claims successful reconstruction of the G-code commands of the final printed objects at 98.89% command-level accuracy and reports that the attack strategy transfers to a second printer in a different environment.

Significance. If the numerical result and its generalization hold under rigorous validation, the work would establish that consumer-grade smartphones suffice for high-accuracy, non-line-of-sight IP extraction from AM systems, thereby demonstrating a practical and low-cost threat vector that current literature has not addressed with commodity hardware.

major comments (2)
  1. [Abstract] Abstract: the central claim of 98.89% command-level reconstruction accuracy is stated without any description of data collection (sampling rates, sensor placement details), feature extraction, classification method, validation procedure (train/test split on G-code sequences or object geometries), or error analysis. This absence makes the numerical result impossible to evaluate against the paper's own evidence and directly undermines assessment of the weakest assumption that the captured emissions contain sufficient unique information for arbitrary G-code sequences.
  2. [Abstract] Abstract: the transferability claim to a second printer is asserted without reporting the accuracy achieved on that printer, whether the model was retrained or transferred zero-shot, the number of distinct commands or objects involved, or controls for environmental differences. These omissions leave the generalization claim unsupported.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We agree that the abstract would benefit from additional high-level details on methodology and results to better support the claims. We will revise the abstract accordingly while preserving its conciseness. Point-by-point responses follow.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of 98.89% command-level reconstruction accuracy is stated without any description of data collection (sampling rates, sensor placement details), feature extraction, classification method, validation procedure (train/test split on G-code sequences or object geometries), or error analysis. This absence makes the numerical result impossible to evaluate against the paper's own evidence and directly undermines assessment of the weakest assumption that the captured emissions contain sufficient unique information for arbitrary G-code sequences.

    Authors: We acknowledge that the abstract, as a concise summary, omits these specifics. The full manuscript details the data collection (smartphone acoustic and magnetic sensors at 60 cm non-line-of-sight), feature extraction, classification approach, validation via train/test splits on G-code sequences and object geometries, and error analysis in the Methods, Experimental Setup, and Results sections, where the 98.89% figure is derived and supported. To address the concern directly, we will revise the abstract to include a brief summary of the sensor modalities, distance/setup, and validation approach. revision: yes

  2. Referee: [Abstract] Abstract: the transferability claim to a second printer is asserted without reporting the accuracy achieved on that printer, whether the model was retrained or transferred zero-shot, the number of distinct commands or objects involved, or controls for environmental differences. These omissions leave the generalization claim unsupported.

    Authors: The abstract summarizes the transferability evaluation but does not include the specific accuracy or experimental parameters for the second printer. The manuscript body reports these details (accuracy on the second printer, retraining/transfer procedure, command/object counts, and environmental controls). We will revise the abstract to state the achieved accuracy on the second printer and note the transfer setting. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical attack demonstration with no derivations or fitted predictions

full rationale

The paper is an empirical side-channel attack study. It collects acoustic and magnetic sensor data from dual smartphones at 60 cm NLOS, then reports command-level G-code reconstruction accuracy of 98.89% on tested objects plus transfer to a second printer. No equations, first-principles derivations, parameter fitting, or predictions are claimed. The central result is a measured success rate on experimental data; it does not reduce to any input by construction, self-definition, or self-citation chain. Evaluation details (train/test splits, command vocabulary) affect claim strength but are not circularity. This matches the default case of a self-contained empirical result.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no technical sections, models, or equations are provided to identify free parameters, axioms, or invented entities.

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