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Predict MT19937 PRNG, from preceding 624 generated numbers. There is a specialization for the 'random' of Python standard library.
usage
install
Aug 19, 2014 Please note that my Mersenne Twister generates 32 bit signed integers whereas the output of Math.random are floating-point numbers between 0 and 1. And a simple bit distribution heatmap (move your mouse, double-click the heatmap to reset). I'm working on implementing R code into C so that it runs faster, but I am having difficulties implementing mersenne twister. I only wish to generate values between (0,1). Here is what I have that.
![Mersenne Mersenne](/uploads/1/2/7/5/127555234/731469454.png)
as a library
This library has the special feature for CPython's standard
random
.Try below one:This is useful for some CTF tasks, e.g. TokyoWesterns CTF 4th 2018: mixed cipher, Tokyo Westerns CTF 3rd: 2017, etc.For more details, read the document.
as a command
Give the 624 32-bit integers, as unsigned decimal integers, line by line.
example (same as tests/cplusplus.py
)
This is an example of a pseudorandom number generator using the Mersenne Twister.
Compile it and let it generate 1000 numbers.
Take the 624 consecutive numbers. We will predict the rest, 376 numbers.
Predict.
Compare the predict numbers and the original ones.If
diff
outputs nothing, it means the prediction was succeeded.reference
thanks to
$ pip install randcrack How it worksThe generator is based upon Mersenne Twister, which is able to generate numbers with excellent statistical properties(indistinguishable from truly random). However, this generator was not designed to be cryptographycally secure.
You should NEVER use in critical applications as a PRNG for your crypto scheme.You can learn more about this generator.This cracker works as the following way. It obtains first 624 32 bit numbers from the generator and obtains the most likely state of Mersenne Twister matrix, which is the internal state. From this point generator should be synchronized with the cracker. How to useIt is important to feed cracker exactly 32-bit integers generated by the generator due to the fact that they will be generated anyway, but dropped if you don't request for them.As well, you must feed the cracker exactly after new seed is presented, or after 624.32 bits are generated since every 624 32-bit numbers generator shifts it's state and cracker is designed to be fed from the begining of some state.
Implemented methodsCracker has one method for feeding: submit(n). After submitting 624 integers it won't take any more and will be ready for predicting new numbers.Cracker can predict new numbers with following methods, which work exactly the same as their siblings from the random module but without predict prefix. These are: predictgetrandbits, predictrandbelow, predictrandrange, predictrandint and predictchoiceNote: Cracker does not implement prediction of random function since it is based on the os.urandom module which is based on /dev/urandom.Here's an example usage.
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