Machine learning inference engine is of great interest to smart edge computing. Compute-in-memory (CIM) architecture has shown significant improvements in throughput and energy efficiency for hardware acceleration. Emerging non-volatile memory technologies offer great potential for instant on and off by dynamic power gating. Inference engine is typically pre-trained by the cloud and then being deployed to the filed. There are new attack models on chip cloning and neural network model reverse engineering. In this paper, we propose countermeasures to the weight cloning and input-output pair attacks. The first strategy is the weight fine-tune to compensate the analog-to-digital converter (ADC) offset for a specific chip instance while inducing significant accuracy drop for cloned chip instances. The second strategy is the weight shuffle and fake rows insertion to allow accurate propagation of the activations of the neural network only with a key.