PRNGs (pseudo-random-number-generators) take in a small amount of randonmness (a seed) and produce a long stream of numbers from that. Anyone using the same PR

PRNGs (pseudo-random-number-generators) take in a small amount of randonmness (a seed) and produce a long stream of numbers from that. Anyone using the same PRNG can look at the output of yours and try to put their PRNG in the same state. If they succeed, the output of theirs will match yours - now and in the future - unless you re-seed. Two problems can occur here: 1) you seed with something they can predict. e.g. seconds since 1970 (or microseconds since 1970). If they have a reasonable sample of numbers from your system, they can try lots of different seeds and see if they can find the one which gives the same output as you. 2) PRNGs have "internal state", which is a bunch of numbers they mix together. Some PRNGs have the property that if you can you can observe enough numbers in a row from the PRNG, you can turn them back into the internal state locally and then you can do the same thing as if you knew the seed (predict future numbers).