I have a program (a game) that uses Java's nextInt function with parameter 0x10000 to get a random 16-bit number.
Java's nextInt() is an 48-bit seed LCG with the following formula:

seed[n+1] = (seed[n] * 0x5DEECE66DL + 0xB) mod (1 << 48)

nextInt() is implemented in such a way that the random number I receive is the current seed's most significant 16 bits when given argument 0x10000. [Java only does this for an argument that is a power of two]

So let me describe the situation: In the game, I get to have two sequential outputs of nextInt(0x10000), R[i] and R[i+1] (which are respectively seed[i] and seed[i+1]'s top 16 bits).

My question is: what information can I extrapolate about the next output of randInt() (R[i+2]) ?

EDIT: I'm in a situation in which a bruteforce of even more than 30,000 steps is not practical..and all I have are these two consecutive outputs. So, is it even feasible extracting some info given these restrictions? (And please be gentle, I don't have any knowledge in math beyond high school)

  • $\begingroup$ Thanks for fixing the statement. An idea: count the entropy in your givens and unknowns, and get an estimate of what remains unknown (that does not sound encouraging for accurately predicting R[i+2]). Ah if you had three values of R[], even non-contiguous... Other idea: the question is similar enough to this one that perhaps the method in that answer can help to compute the exact distribution of the possible R[i+2] given R[i] and R[i+1], assuming flat random seed[0]; I don't know for sure. $\endgroup$
    – fgrieu
    Commented Jan 28, 2014 at 7:09
  • 1
    $\begingroup$ With above hypothesis, you can conclude something like: if R[i]=42 and R[i+1]=2014, then for certain R[i+2] is not among {0, 4, 9, 12, 13, 21, 24, 33, 38, 45, 46, 54, 68, 71, 80, 83, 92, 95, 100, 104, ..}, and it has odds 3/65537 to be among {201, 888, 1517, 1959, 2402, 3031, 3717, 4160, 4346, 4789, 5231, 6547, 6989, ..}. In a game, that could help. My quick and dirty brute program to compute such statistics runs in seconds for any given (R[i], R[i+1]) pairs (it is markedly above your 30k steps bound, but likely that can be improved by the method I pointed to). $\endgroup$
    – fgrieu
    Commented Jan 28, 2014 at 20:08
  • 1
    $\begingroup$ You say algorithms that involve 30,000 steps are not feasible. Frankly: I'm super-skeptical about that claim. Have you tried implementing such a thing? Such a computation will probably finish in microseconds. I find it hard to imagine that this is too slow. $\endgroup$
    – D.W.
    Commented Jan 29, 2014 at 3:03
  • $\begingroup$ Correction: in my above comment, I wish there would be: it has odds 3/65537 to be any of the values in {201, 888, 1517, 1959, 2402, 3031, 3717, 4160, 4346, 4789, 5231, 6547, 6989, ..}; these are the smaller exceptionally likely values for R[i+2], assuming flat distribution of seed , R[i]=42 and R[i+1]=2014. $\endgroup$
    – fgrieu
    Commented Jan 29, 2014 at 6:52
  • $\begingroup$ Zerith, is there anything missing from D.W.'s answer? Because IMHO it could be marked as accepted answer otherwise. $\endgroup$
    – Maarten Bodewes
    Commented Jul 20, 2015 at 13:29

1 Answer 1


Assuming this generator is well-seeded, you probably can't learn much about the next output. You observe two outputs, so 32 bits in total. The state of the generator is 48 bits. Thus, there will probably be about $2^{16}$ states of the generator that are compatible with your two observations. This means that most of the 16-bit values for the next output are (heuristically) likely to be possible.

Thus, your best bet might be to look at how the generator is seeded. I have a vague recollection that Java's PRNG is poorly seeded, using a seed that has less than 48 bits of entropy. If this is the case, then it might be possible to predict the next output, by enumerating candidates for the initial seed and seeing which ones are consistent with the outputs you've observed.

If you need to enumerate which values for the next output are possible, here is a way to do it.

The naive way is to enumerate all $2^{32}$ possible values of seed[i] (you know its top 16 bits, thanks to your knowledge of R[i], so you just need to enumerate all possibilities for its low 32 bits), then test which ones are compatible with the observed value of R[i+1], and for each one that survives, compute the resulting value of R[i+2]. This takes about $2^{32}$ steps of computation, so you can probably make it run in a few seconds or tens of seconds of computation power.

With a little bit of cleverness, you can make this run a lot faster. Since we know the value of R[i+1], we know the range of possible values for seed[i+1]: in particular, we know that 2^32 * R[i+1] <= seed[i+1] <= 2^32 * R[i+1] + 2^32 - 1. Also, we know that seed[i+1] = 0x5DEECE66D * seed[i] + 11 - c * 2^48 for some c in the range 0x5DEEC * R[i] <= c <= 0x5DEED * (R[i]+1). Moreover, we know the top 16 bits of seed[i], so we can write seed[i] = 2^32 * R[i] + x, for some integer x satisfying 0 <= x <= 2^32 - 1.

Combining all of this, it follows that

2^32 * R[i+1] <= 0x5DEECE66D * 2^32 * R[i] + 0x5DEECE66D * x + 11 - c * 2^48 <= 2^32 * R[i+1] + 2^32 - 1

or equivalently,

(2^32 * R[i+1] - 0x5DEECE66D00000000 * R[i] - 11 - c * 2^48)/0x5DEECE66D <= x <= (2^32 * R[i+1] - 0x5DEECE66D00000000 * R[i] + 2^{32} - 12 - c * 2^48)/0x5DEECE66D

This range is of width less than 1, so if you know the left-hand-side and the right-hand-side, you can immediately infer x.

Thus, if we know c, then the value of x is determined, which reveals seed[i] and thus the next output of the generator. Finally, there are only about 0x5DEED$\approx 380,000$ possible values of c.

This gives an algorithm that enumerates all possible values for the next output, using about $380,000$ steps of computation. We enumerate over all $380,000$ possible values of c; for each possible value of c, we round (2^32 * R[i+1] - 0x5DEECE66D00000000 * R[i] + 2^{32} - 12 - c * 2^48)/0x5DEECE66D down to the nearest integer, call it x, and then we infer the value of seed[i] (namely, seed[i] = 2^32 * R[i] + x) and run the generator for two steps to observe the implied next output. We collect all possible next outputs of the generator. This gives us the set of all possible values for the next output. Each iteration uses just a few simple arithmetic operations, and we do $380,000$ iterations, so this procedure should run extremely fast: in milliseconds or less.

  • $\begingroup$ The results that I reported are per the naive way in this answer; run time is about 7 seconds on an old CPU, so D.W.'s estimates are excellent. Adding a little bit of cleverness would bring runtime to a blink. $\endgroup$
    – fgrieu
    Commented Jan 29, 2014 at 5:10

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