The increasing ubiquity of material-extrusion-based additive manufacturing is motivating cybersecurity researchers to explore its offensive and defensive landscape. Being a physical system, 3D printers have non-zero tolerance specifications for precision and trueness parameters. While a single-bit change in a digital data file is sufficient to fail its integrity and is easily detected through methods such as hashing, the printing process (and subsequently the printed object) remains compliant within the tolerance zone. This study systematically analyzes the material extrusion process and identifies four attack opportunities where low-magnitude kinetic cyberattacks exploit the physical process compliance zone to sabotage the printed part’s mechanical properties. The attacks are demonstrated on ASTM-compliant tensile and flexure bars through a man-in-the-middle attack scenario by hijacking the network layer communication between the 3D printer and the printer control machine. The physically stealthy attacks did not produce any evident deformation in the parts’ dimensions and mass, while the destructive tests confirm that they are still effective in modifying the tensile and bending strength by up to 25%. The effectiveness of the attacks in bypassing the defenses is assessed by implementing one of the leading detection schemes described in the current literature. The attacks were either not detected at all or detected with a significantly high false negative rate at various attack magnitudes.

Additive manufacturing (aka, 3D printing) is increasingly used in bioprinting to create objects layer by layer from ground zero. As research and development progress in bioprinting technology for medical applications, ensuring the security of 3D bioprinters against adversarial attempts becomes critical. This paper proposes six novel sabotage attacks on two types of bioprint constructs as case studies, i.e., a multilayered square box and a human ear, to show that attackers can deliberately manipulate the bioprinting process to sabotage a bioprinted construct. We use quality assurance metrics, i.e., printability and cell viability, to demonstrate the impact of these attacks on the printed constructs. Furthermore, this paper introduces BioSaFe, a bioprinting security framework for real-time monitoring of critical printing parameters per-layer basis, including nozzle temperature, layer thickness, UV curing, HEPA filter status, print geometry, and print speed. BioSaFe employs spatiotemporal modeling and interpolation functions to compare in-situ sensing data with a reference G-code file (being used for printing) in both space and time domains. This direct comparison does not require a training phase on printed objects and enables BioSaFe to start monitoring from the first printing job, supporting Industry 4.0 for mass customization. Our evaluation results show that BioSaFe can accurately detect our sabotage attacks, demonstrating its potential in safeguarding bioprinting processes.

Additive Manufacturing (AM) is critical for the fourth industrial revolution (i.e., Industry 4.0). It involves printing a 3D object layer-by-layer from scratch. Fused filament fabrication (FFF), one of the most widely used AM technology, has been adopted by commercial and domestic consumers. With the recent addition of metal filaments, FFF caters to a broad spectrum of manufacturing industry requirements. Cybersecurity and Quality Assurance (QA) of the FFF process is an active research area. Like any other cyber-physical system, FFF exhibits many side channels (SCs), including acoustic and thermal emissions, vibrations, etc. Researchers in the QA domain use SCs to predict defects in the printed parts. Cybersecurity researchers, on the other hand, utilize SCs to identify malicious anomalies in the process. While the aims are different, there are definite overlaps in both communities’ acquisition and analysis methodologies. As the two communities bring distinct skill sets and expertise, we find an opportunity to bring them closer through a systematic study of available work and identifying the commonalities and distinctions to motivate the consumption of cross-domain knowledge. Our approach to systematizing the knowledge is based on identifying the available SC, the acquisition and analysis methodologies, performance statistics, associated challenges, and future research directions. This knowledge consolidation and systematization exercise will not only help the new researchers aiming to explore SCs in the FFF process but also highlight collaboration opportunities between QA and cybersecurity communities.