33C3: How Can You Trust Your Random Numbers?

One of the standout talks at the 33rd Chaos Communications Congress concerned pseudo-random-number generators (PRNGs). [Vladimir Klebanov] (right) and [Felix Dörre] (left) provided a framework for making sure that PRNGs are doing what they should. Along the way, they discovered a flaw in Libgcrypt/GNUPG, which they got fixed. Woot.

mpv-shot0012-zoomCryptographically secure random numbers actually matter, a lot. If you’re old enough to remember the Debian OpenSSL debacle of 2008, essentially every Internet service was backdoorable due to bad random numbers. So they matter. [Vladimir] makes the case that writing good random number generators is very, very hard. Consequently, it’s very important that their output be tested very, very well.

So how can we test them? [Vladimir] warns against our first instinct, running a statistical test suite like DIEHARD. He points out (correctly) that running any algorithm through a good enough hash function will pass statistical tests, but that doesn’t mean it’s good for cryptography.

Instead, there are two ways to test a PRNG. First, if you’re using a standard function and have a set of reference seeds and outputs, check them. At least you’ll verify that your code is doing what it should. Secondly, and more generally, you want to make sure that the algorithm isn’t losing entropy — PRNGs don’t create randomness, so the best they can do is not lose it.

Here are some things you can check. If a part of the seed doesn’t influence the output, or if two seeds produce the same output, or equivalently if there are fewer possible outputs than seeds, the algorithm is losing entropy. If you can run through an arbitrarily large number of seeds and outputs, you could possibly brute-force this test (hopefully before the universe dies its inevitable heat death).

Or you could do it analytically. They test six PRNG implementations using the CBMC and Minisat static analysis tools to test for these requirements. Doing so caught all of the problems that they were expecting, and one that they didn’t. Using their “entroposcope”, they trace the loss of entropy through the flow of the program, find the bug, and save the day.

This isn’t a beginners talk on cryptographic PRNGs, but it’s a darn good one. PRNGs that look random, and pass statistical tests, can still lose entropy. As the pair pointed out in the question and answer, the only way to be sure within a reasonable amount of time is to dig through the code and verify that it’s not. The implication of this is that the only secure PRNG is an open-source PRNG. But you knew that already, right?

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