I'm assuming by iterate you mean "generate outputs of a GAN which preserve some order that you can iterate through."
The answer is yes, but not in the way that one might think. Despite the fact that we seed the GAN with input noise, there is no guarantee that the GAN makes use of this at all. This is a theme with GANs: we often want to imbue them with prior knowledge that we think is important, but is easily ignored by the GAN. In this case, we want to generate samples from p(x|z), where x is in the space of our data (often images, in this case passwords), but provided it gets good results according the the loss function, your GAN may learn p(x|z)=p(x). This is fine if you don't care about enforcing some relationship between input and generated samples, but here we do.
One solution is to use InfoGAN (https://arxiv.org/abs/1606.03657), which adds a term to the loss function that the mutual information between a latent code and the generator output must be high. Your latent code might be drawn from a uniform distribution on [-1,1], and the generator output will be conditioned on this code. This being continuous, it's questionable what "iterate" might mean. On a computer, maybe you iterate through every possible float (as someone mentioned), but if you want to generate N different samples, you could also discretize this distribution to N values on the given interval, each with probability 1/N and sample from this PMF.
The generator is passed random noise from a distribution, so iterating through all the samples is the same as just sampling from that distribution a ton.
If your noise is uniform on [0, 1], then, sure, you could just iterate through every possible float, I guess. Though in practice you're talking an astronomical number of possible combinations.
Another option is to take two samples from the distribution and interpolate in-between them, which might give you an idea of the distribution of samples. Maybe, it depends on the GAN and data.
A GAN effectively takes a seed to generate output, normally people provide random inputs, but you could iterate on the seed. Not sure how good your output would be though.
The answer is yes, but not in the way that one might think. Despite the fact that we seed the GAN with input noise, there is no guarantee that the GAN makes use of this at all. This is a theme with GANs: we often want to imbue them with prior knowledge that we think is important, but is easily ignored by the GAN. In this case, we want to generate samples from p(x|z), where x is in the space of our data (often images, in this case passwords), but provided it gets good results according the the loss function, your GAN may learn p(x|z)=p(x). This is fine if you don't care about enforcing some relationship between input and generated samples, but here we do.
One solution is to use InfoGAN (https://arxiv.org/abs/1606.03657), which adds a term to the loss function that the mutual information between a latent code and the generator output must be high. Your latent code might be drawn from a uniform distribution on [-1,1], and the generator output will be conditioned on this code. This being continuous, it's questionable what "iterate" might mean. On a computer, maybe you iterate through every possible float (as someone mentioned), but if you want to generate N different samples, you could also discretize this distribution to N values on the given interval, each with probability 1/N and sample from this PMF.