Memory faults affecting Neural Networks (NNs) are a critical source of vulnerability in modern AI systems. Estimating realistic failure rates is challenging because most software-side assessments do not capture how multiple, concurrent faults manifest in practice, and the space of multi-fault configurations grows combinatorially. This thesis introduces a Multiple Statistical Fault Injection (MSFI) framework that provides statistically sound failure-rate estimates for neural networks under multiple simultaneous weight faults, while keeping the number of injected faults and the memory footprint under control. The method targets application-level vulnerability: multiple bit-flip or stuck-at faults are injected into the network weights, and the resulting behavior is measured on the designated test set.