Abstract
Microfluidic biochips (MBs) are transforming diagnostics, healthcare, and biomedical research. However, their rapid deployment has exposed them to diverse security threats, including structural tampering, material degradation, sample-level interference, and intellectual property (IP) theft, such as counterfeiting, overbuilding, and piracy. This perspective highlights emerging attack vectors and countermeasures aimed at mitigating these risks. Structural attacks, such as stealthy design code modifications, can result in faulty diagnostics. To address this, deep learning -based anomaly detection leverages microstructural changes, including optical changes such as shadows or reflections, to identify and resolve faults. Material-level countermeasures, including mechano-responsive dyes and spectrometric watermarking, safeguard against subtle chemical alterations during fabrication. Sample-level protections, such as molecular barcoding, ensure bio-sample integrity by embedding unique DNA sequences for authentication. At the IP level, techniques like watermarking, physically unclonable functions, fingerprinting, and obfuscation schemes provide robust defenses against reverse engineering and counterfeiting. Together, these approaches offer a multi-layered security framework to protect MBs, ensuring their reliability, safety, and trustworthiness in critical applications.