  Similar to the revolution of open source code sharing, Artificial
Intelligence (AI) model sharing is gaining increased popularity. However, the
fast adaptation in the industry, lack of awareness, and ability to exploit the
models make them significant attack vectors. By embedding malware in neurons,
the malware can be delivered covertly, with minor or no impact on the neural
network's performance. The covert attack will use the Least Significant Bits
(LSB) weight attack since LSB has a minimal effect on the model accuracy, and
as a result, the user will not notice it. Since there are endless ways to hide
the attacks, we focus on a zero-trust prevention strategy based on AI model
attack disarm and reconstruction. We proposed three types of model
steganography weight disarm defense mechanisms. The first two are based on
random bit substitution noise, and the other on model weight quantization. We
demonstrate a 100\% prevention rate while the methods introduce a minimal
decrease in model accuracy based on Qint8 and K-LRBP methods, which is an
essential factor for improving AI security.
