Cybersecurity in Additive Manufacturing Systems

As additive manufacturing (AM) evolves from prototyping into large-scale production, the convergence of digital design, networked fabrication equipment and global supply chains has created a new cyber-physical frontier. Unlike traditional subtractive processes, AM relies on a continuous digital thread that links computer-aided design files, process parameters and machine controls. This seamless integration exposes multiple points of entry for malicious actors seeking to tamper with part geometry, embed hidden defects or exfiltrate intellectual property. Key threats include sabotage attacks that subtly alter internal structures without changing external appearance, reverse engineering of proprietary designs, counterfeiting of safety-critical components and data-theft across cloud-based storage. The inherently distributed nature of AM supply chains further amplifies risk, as vulnerabilities at a single vendor or in a shared file repository can propagate throughout the network. Securing AM therefore demands a holistic approach that spans authentication of design files, real-time monitoring of machine behaviour, robust encryption of digital assets and provenance tracking of finished parts. Advances in machine-learning-based anomaly detection, non-invasive side-channel monitoring and intrinsic material coding are beginning to bridge gaps in existing cybersecurity frameworks. As industries such as aerospace, medical devices and defence increasingly adopt AM for high-value components, the development of standards and resilient architectures has become both a scientific imperative and a practical necessity to ensure safety, integrity and trust in tomorrow’s manufacturing ecosystems.

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Researchers have characterised the additive manufacturing supply chain as a holistic cyber-physical system, emphasising the interdependence of raw materials, printer hardware and virtual workflows. This viewpoint highlights how a single compromised design file or unauthorised modification in the digital thread can introduce structural defects, propagate counterfeit parts and undermine the integrity of end-use components. By proposing a risk classification scheme, this work underlines the need to evolve traditional IT security measures into domain-specific frameworks that address unique AM attack vectors.

To detect subtle sabotage attacks, a multi-modal monitoring approach has been developed that fuses data from motion, acoustic and thermal side-channels. By analysing mutual information between each channel and the machine’s control parameters, this system achieves high detection accuracy across diverse attack scenarios. The study demonstrates that combining multiple sensing modalities significantly outperforms uni-modal strategies, enabling near-real-time flagging of anomalies without interrupting the printing process.

An alternative strategy focuses on embedding material-inherent codes into metal parts through controlled variations in powder-bed fusion and directed energy deposition. These unique, physically unclonable fingerprints can be authenticated using non-destructive eddy-current measurements, rendering counterfeiting virtually impossible. The approach exploits natural fluctuations in melt-pool dynamics to generate distinctive signature patterns, providing a rapid, low-cost method for provenance tracking and anti-counterfeiting in safety-critical applications.

Cybersecurity in Additive Manufacturing Systems publication trend

The graph below shows the total number of articles in cybersecurity in additive manufacturing systems across all publications each year (not limited to Nature Index journals).

Technical terms

Additive Manufacturing (AM): A process of creating three-dimensional objects by layer-by-layer deposition of material directly from digital models.

Cyber-Physical System (CPS): An integrated network of computational algorithms and physical components whose operations are tightly coupled and interact in real time.

Digital Thread: The end-to-end linkage of data spanning design, simulation, fabrication and inspection, forming a continuous informational pipeline.

Sabotage Attack: A malicious intervention in the manufacturing process that introduces imperceptible defects or parameter deviations to compromise part performance.

Side-Channel Analysis: A security technique that infers system states or detects tampering by monitoring indirect signals such as power consumption, acoustics or thermal emissions.

References

1. Additive Manufacturing Cyber-Physical System: Supply Chain Cybersecurity and Risks. IEEE Access (2020).
2. Sabotage Attack Detection for Additive Manufacturing Systems. IEEE Access (2020).
3. Unique coding for authentication and anti-counterfeiting by controlled and random process variation in L-PBF and L-DED. Additive Manufacturing (2020).