Florian Weimer writes:
> I've got a function f : S -> X x S where S = (Z/2Z)**96 and
> X = (Z/2Z)**32.  Suppose that s_0 is fixed and (x_i, s_i) = f(s_{i-1}).
> (f implements a PRNG.  The s_i are subsequent internal states and the
> x_i are results.)
>
> Now f happens to be linear.  I know the values of x_i, x_{i+1}, ...,
> x_{i+k} modulo N, [where N = 28233].
> Is it possible to recover s_i with reasonable effort (better than brute
> force, and k should be in the hundreds, not thousands)?

Yes.  I will show two attacks.  The results: Attack #1 is a
straightforward improvement to exhaustive search, and takes 2^52 work (~
10-20 CPU-years?).  Attack #2 is a more sophisticated meet-in-the-middle
attack, and takes 2^37 work and 2^35 space; the best instantiation I've
found looks like it would involve 16GB of RAM and a few days or weeks
of computation.  Both attacks recover the entire state of the PRNG,
given only a handful of known outputs.  It's possible there may be
other, better attacks.

My conclusion is that this PRNG is not cryptographically strong and
should not be used anywhere that you may face an adversary who is
motivated to break the PRNG.  In short, this PRNG is broken.


Attack #1: Given x mod N, there are only about 2^32/28233 = 2^17.2
possibilities for x, and it's easy to enumerate them all.  Suppose we
know x_1 mod N, x_2 mod N, ..., x_7 mod N.  Enumerate all
(2^17.2)^3 = 2^51.6 possibilities for x_1, x_2, and x_3.  For each
such possibility, you know 96 bits of output from a linear system,
and there are 96 unknowns, so you can solve for s_0.  Given s_0,
you can compute the predicted value of x_4, .., x_7 and compare them
to the observed values of x_4 mod N, etc.  If everything matches,
you've found the original seed s_0 and broken the PRNG.

This requires trying 2^51.6 possibilities, so it's a bit less than
2^51.6 work.  That's already a small enough number that the system
is not cryptographically secure.

This can be sped up slightly by noting that x_4 is a linear function
of x_1,x_2,x_3.  We can precompute the form of that linear function
and express it as a 32x96 matrix.  The best approach I can see will take
something like 500 CPU cycles to compute x_4 from x_1,..,x_3, so I'd
expect the total number of CPU cycles needed at something like 2^60.6
or so.  On a 4GHz machine, that's like 13 CPU-years.  Of course it is
trivially parallelizable.


Attack #2: If we were given inferred values of x_1, x_2, x_3, and x_4,
we could check them for consistency, since they represent 128
equations in 96 unknowns.  In particular, we can find a linear function
g from 128 bits to 32 bits such that g(x_1,x_2,x_3,x_4) = 0 if
x_1,..,x_4 are consistent with having been produced by this PRNG.
In fact, this can be broken down further, so that if x_1,..,x_4
are consistent, they will satisfy this equation:
  h(x_1,x_2) = h'(x_3,x_4).
Call this the check equation.  Here h,h' are linear functions from 64
bits to 32 bits, so if x_1,..,x_4 are not consistent, they have only a
2^-32 chance of satisfying this check equation.

Suppose we are given x_1 mod N, .., x_8 mod N.  We'll enumerate all
2^34.4 possibilities for x_1,x_2, compute h(x_1,x_2) for each, and
store them in a hash table or sorted list keyed on the 32-bit value
of h(x_1,x_2).  Once that table is built, we'll enumerate all 2^34.4
possibilities for x_3,x_4, compute h'(x_3,x_4) for each, and find
all occurrences in the table where
  h(x_1,x_2) = h'(x_3,x_4).
On average, we expect to find about 2^34.4/2^32 = 2^2.4 matches.
For each such match, compute the predicted value of x_5 as a function
of x_1,..,x_4 and see whether it agrees with the observed value of
x_5 mod N, etc., to weed out false matches.  In total, you'll need
to explore 2^34.4 + 2^34.4*2^2.4 ~= 2^37 possibilities, and you'll
need space for 2^34.4 entries in the table.

We can store the table as a sorted list.  This list will take up about
1600 GB, and you could buy enough hard disks for that at ~ $2000, say.
You can add an index in memory that tells you, for each value of
h(x_1,x_2), which block or sector on disk those entries of the list is
found at.  You'll need to make 2^34.4 random seeks into this hard disk
array.  With good disk head scheduling algorithms you might be able to
get the seek time down a bit, but even optimistically we're probably
still talking about a year of computation.

Fortunately, we can trade time for space.  With 16GB of RAM, we can
store 1/128th of the list: namely, all values where the low 7 bits
of h(x_1,x_2) are some fixed value V.  We cycle through all choices
of V, repeating the attack once for each V.  With this choice, I expect
the amount of time spent waiting for RAM to be comparable to the CPU
cost of the attack.  For each V, we have to evaluate a linear function
2^35.4 times.  It looks to me like this will take a few CPU-days.


Disclaimers: I have not checked any of the above analysis.  I have
not implemented it to test whether these attacks actually work.  Even
if the attacks do work, my estimates of the resources needed for the
attacks are a very rough estimate and should be taken with a large
grain of salt.  I didn't have time to do anything more than a cursory
analysis, so if there are errors in the above, please forgive me.

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