Question

When working on a large software project I often use fuzz-testing as part of my test cases to help smoke out bugs that may only show up when the input reaches a certain size or shape. I’ve done this most commonly by just using the standard random number facilities that are bundled with the programming language I happen to be using.

Recently I’ve started wondering, ignoring the advantages or disadvantages of fuzz testing in general, whether or not it’s a good idea to be using non-cryptographically secure pseudorandom number generators when doing fuzz testing. Weak random number generators often exhibit patterns that distinguish them from true random sequences, even if those patterns are not readily obvious. It seems like a fuzz test using a weak PRNG might always fail to trigger certain latent bugs that only show up in certain circumstances because the pseudorandom numbers could be related to one another in a way that never trigger those circumstances.

Is it inherently unwise to use a weak PRNG for fuzz testing? If it is theoretically unsound to do so, is it still reasonable in practice?

Answer 1

You’re confusing two very different grades of “weakness”:

• statistical weakness means the output of the PRNG exhibits statistical patterns, such as having certain sequences occur more often than others. This could actually lead to ineffective fuzz testing in some rare cases. Statistically strong PRNGs are performant and widely available though (most prominently the Mersenne Twister).
• cryptographical weakness means that the output of the RNG is in some way predictable given knowledge other than the seed (such as the output itself). It makes absolutley no sense to require a PRNG used for fuzz testing to be cryptographically strong, because the “patterns” exhibited by statistically-strong-but-cryptographically-weak PRNGs are pretty much only an issue if you need to prevent a cryptographically versed attacker from predicting the output.