  With the steady rise of the use of AI in bio-technical applications and the
widespread adoption of genomics sequencing, an increasing amount of AI-based
algorithms and tools is entering the research and production stage affecting
critical decision-making streams like drug discovery and clinical outcomes.
This paper demonstrates the vulnerability of AI models often utilized
downstream tasks on recognized public genomics datasets. We undermine model
robustness by deploying an attack that focuses on input transformation while
mimicking the real data and confusing the model decision-making, ultimately
yielding a pronounced deterioration in model performance. Further, we enhance
our approach by generating poisoned data using a variational autoencoder-based
model. Our empirical findings unequivocally demonstrate a decline in model
performance, underscored by diminished accuracy and an upswing in false
positives and false negatives. Furthermore, we analyze the resulting
adversarial samples via spectral analysis yielding conclusions for
countermeasures against such attacks.
