The exploding gradient problem occurs in neural networks when large error gradients accumulate during backpropagation, causing the model's weights to become unstable and often leading to numerical overflow. This issue is particularly prevalent in deep networks and recurrent neural networks, making training difficult and often requiring gradient clipping or other techniques to manage it.