AVOID: Automated Void Detection in STL Files

Additive manufacturing is a multi-billion dollar industry 21.58 billion in 2024), so its processes should be dependable and secure. Malicious actors can inject negative spaces, known as voids, in STL files, which can have a devastating impact on a final product's quality. Current ways of detecting voids use machine sensor or simulation data, and physical verification measures after printing. However, to the best of our knowledge, no method exists for detecting hidden voids solely at the STL level. Void detection at this level is inexpensive, and has the potential to detect voids in designs en masse. In this work, we propose Avoid, a new approach to detect hidden voids. It both detects voids, and assesses their risk of weakening the manufactured part. Avoid performs risk assessment based on the size and location of each detected void. We empirically evaluated Avoid using several large datasets, in total thousands of STL files, each with multiple hidden voids. We found that Avoid is highly accurate, with 97.7% recall, 98.1% precision, and 99% F1 on average across six data sets. Avoid is also robust, scalable, and efficient. We find that high risk voids account for approximately 7% of all detected voids inserted randomly.