Engineering Attack Vectors and Detecting Anomalies in Additive Manufacturing

TL;DR

This paper explores cyberattack vulnerabilities in additive manufacturing, specifically targeting FDM 3D printers through MitM attacks on G-code files, and proposes an unsupervised Transformer-based intrusion detection system to identify anomalies.

Contribution

It introduces a novel threat model for FDM printers and develops an unsupervised IDS using Transformer encoders and autoencoders for anomaly detection in 3D printing.

Findings

The proposed IDS effectively detects stealthy MitM attacks.

Transformer-based semantic analysis improves anomaly detection accuracy.

Experimental results show high precision in distinguishing compromised prints.

Abstract

Additive manufacturing (AM) is rapidly integrating into critical sectors such as aerospace, automotive, and healthcare. However, this cyber-physical convergence introduces new attack surfaces, especially at the interface between computer-aided design (CAD) and machine execution layers. In this work, we investigate targeted cyberattacks on two widely used fused deposition modeling (FDM) systems, Creality's flagship model K1 Max, and Ender 3. Our threat model is a multi-layered Man-in-the-Middle (MitM) intrusion, where the adversary intercepts and manipulates G-code files during upload from the user interface to the printer firmware. The MitM intrusion chain enables several stealthy sabotage scenarios. These attacks remain undetectable by conventional slicer software or runtime interfaces, resulting in structurally defective yet externally plausible printed parts. To counter these stealthy threats, we propose an unsupervised Intrusion Detection System (IDS) that analyzes structured machine logs generated during live printing. Our defense mechanism uses a frozen Transformer-based encoder (a BERT variant) to extract semantic representations of system behavior, followed by a contrastively trained projection head that learns anomaly-sensitive embeddings. Later, a clustering-based approach and a self-attention autoencoder are used for classification. Experimental results demonstrate that our approach effectively distinguishes between benign and compromised executions.