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Reverse-engineering Linear PRNG with Exploratory Seeding

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21 Sep 2023CPOL11 min read 7.2K   51   4   2
How to calculate transition matrix for linear pseudo-random number generator manipulating its internal state
This article describes a method to determine the transition matrix of linear pseudo-random number generator without prior detailed knowledge of its algorithm. The only information needed is: state vector dimensionality, nature of its components and a single-step procedure used as a black box to transform one state to another.


Linear pseudo-random number generators (PRNGs) are quite popular in applications where security is not an issue: they are fast and, often more importantly, allow doing some tricks beyond simply producing a stream of random numbers, such as jumping along this stream in both directions with logarithmic complexity. This can be particularly useful when we need to distribute and/or parallelize our computations.

Algorithms performing such jumps are based on the common theory which is explained, for example, in my previous articles: System.Random and Infinite Monkey Theorem or XorShift Jump 101. However, we must do some preparatory steps before applying this common theory, and those steps are non-trivial and depend on particular PRNG type.

Naturally, this raises the question: can we do better? Fortunately, the answer is positive: we indeed can generalize further and make the entire process less PRNG-dependent.


It is assumed that the reader is familiar with linear PRNG theory basics explained in System.Random and Infinite Monkey Theorem. Unlike that article, the demo code for this one is written in C++ 11 so the reader should be fluent in this dialect at the intermediate level.

The code has been intentionally written as library-independent as possible, except for Google Test 1.8.1 which is used for unit testing infrastructure.

Building the demo requires G++ with accompanying toolchain, such as Cygwin (which has been actually used) but any modern Linux environment should also fit well.

Exploratory Seeding: The Idea

Let's begin from the short recap of the theory. As we could see from previous articles, transition function [2] of linear PRNG can be represented as:

$ s^{(i+1)} = T s^{(i)}, $

where \(T\) is some matrix which could be named as transition matrix and both \(s^{(i+1)}\) and \(s^{(i)}\) are \(N\)-dimensional vectors belonging to the set \(S\) of states.

This transition matrix differs from one PRNG algorithm to another and we must figure it out. While for lagged Fibonacci generator, this matrix is quite obvious and could be simply written down, doing the same for xorshift required writing a program to perform symbolic transformations over formulas specifying some particular PRNG from the xorshift family.

Let's consider now a special value of \(s=\left\langle 1, 0, \ldots, 0\right\rangle\). In this case

$ \begin{multline*} T s = \begin{pmatrix} t_{11} & t_{12} & \ldots & t_{1N} \\ t_{21} & t_{22} & \ldots & t_{2N} \\ \vdots & \vdots & \ddots & \vdots \\ t_{N1} & t_{N2} & \ldots & t_{NN} \end{pmatrix} \begin{pmatrix} 1 \\ 0 \\ \vdots \\ 0 \end{pmatrix} = \\ = \begin{pmatrix} t_{11} \cdot 1 + t_{12} \cdot 0 + \cdots + t_{1N} \cdot 0 \\ t_{21} \cdot 1 + t_{22} \cdot 0 + \cdots + t_{2N} \cdot 0 \\ \vdots \\ t_{N1} \cdot 1 + t_{N2} \cdot 0 + \cdots + t_{NN} \cdot 0 \\ \end{pmatrix} = \begin{pmatrix} t_{11} \\ t_{21} \\ \vdots \\ t_{N1} \end{pmatrix}. \end{multline*} $

Doing the same for \(s=\left\langle 0, 1, 0, \ldots, 0\right\rangle\), we can see the pattern:

$ \begin{multline*} T s = \begin{pmatrix} t_{11} & t_{12} & \ldots & t_{1N} \\ t_{21} & t_{22} & \ldots & t_{2N} \\ \vdots & \vdots & \ddots & \vdots \\ t_{N1} & t_{N2} & \ldots & t_{NN} \end{pmatrix} \begin{pmatrix} 0 \\ 1 \\ 0 \\ \vdots \\ 0 \end{pmatrix} = \\ = \begin{pmatrix} t_{11} \cdot 0 + t_{12} \cdot 1 + t_{13} \cdot 0 + \cdots + t_{1N} \cdot 0 \\ t_{21} \cdot 0 + t_{22} \cdot 1 + t_{23} \cdot 0 + \cdots + t_{2N} \cdot 0 \\ \vdots \\ t_{N1} \cdot 0 + t_{N2} \cdot 1 + t_{23} \cdot 0 + \cdots + t_{NN} \cdot 0 \\ \end{pmatrix} = \begin{pmatrix} t_{12} \\ t_{22} \\ \vdots \\ t_{N2} \end{pmatrix}. \end{multline*} $

That is, if we set the PRNG state to \(\left\langle 1, 0, \ldots, 0\right\rangle\) and apply the transition function to this state, the subsequent state will be equal to the first column of the corresponding transition matrix. Repeating this procedure \(N\) times for unit value running across the state vector, we can collect the entire matrix \(T\). Effectively, we calculate each column of the matrix product \(T I = T\) , obtaining value of \(T\) as a result.

This technique is so obvious that I would never claim I invented it first. It must be definitely used somewhere else in the past, but, unfortunately, I couldn't find any academic parer to reference.

Coming back to techspeak, initializing the PRNG with some state is known as "seeding" and, since we have done it with a goal to obtain some knowledge about the PRNG internals, it could be considered an exploration as well. This explains the reason behind calling the entire process an exploratory seeding.

Comparing this approach to our earlier ways of getting the transition matrix, we can see that it needs much less information about the PRNG algorithm: indeed, this PRNG still has to be linear, have an ability to set its internal state vector and get it back but it is no longer necessary to know the precise formula for transition function. In fact, we can even reverse engineer some PRNG presented to us as a kind of "black box". However, in real life, the need to perform such reverse engineereing is extremely rare: you can always find some trustworthy PRNG algorithm which is properly described. Therefore, the main value of exploratory seeding is its ability to get transition matrix with less efforts rather than unravel the mysteries.

Software Implementation

The source code accompanying this article is aimed to provide a short proof-of-concept demo rather than some elaborate library. Therefore, I will trade an efficiency for simplicity and clarity when appropriate.

The entire code consists of two parts: for beginning, we will implement some popular PRNG algorithms to show how the exploratory seeding approach could be generalized. The second part, the core of the method, will be a generic implementation of the seeding procedure, applicable to all of them.

So far, our sample PRNGs are: one of so called multiplicative congruential PRNGs (MCGs), namely ``Minimal Standard'' MCG proposed by Park and Miller and described in [4, § 7.1], one representative of the lagged Fibonacci generator family from [1, § 3.2.2] and, finally, 128-bit xorshift [3], our workhorse from my previous article.

A word of caution: these generators are only chosen for this article due to their simplicity and variety of approaches. They do not represent a state-of-art in pseudorandom number generation, have known issues and, therefore, not recommended to use in production.

The project structure is quite simple: each PRNG type is defined in separate header file with appropriate name (park_miller_mcg.h, and so on). Header file tr_utils.h contains an implementation of the exploratory seeding itself. Due to extensive use of C++ templates, the entire class definitions are present in these header files. Finally, the only .cc file in this project,, contains unit tests checking transition matrix correctness for each PRNG family mentioned above.

The PRNG implementations are, to some extent, incomplete. Indeed, since we are more interested in manipulations with the generator state, we don't have to implement the output function [2]. Therefore, the public interface of PRNG classes is limited to only two functions: init(), which initializes the PRNG state from PRNG-specific data parameter, and step_fwd() which advances this generator internal state by one forward step.

This public PRNG interface, however, is not enough for exploratory seeding. Indeed, we need a way of reading the PRNG internal state and setting it back. Even more, this way should be unified so we will be able to develop generic code. It is natural to define two complementary functions: get_state() and set_state(), respectively. But how should we do that satisfying our requirement? One possible solution would be adding such functions directly to class representing each PRNG but this functionality is quite specific and not aimed for ordinary use. Another approach which allows us to separate code by its intended audience is known as type traits.

The basic idea is very simple: let's move auxiliary functionality to separate companion class. Note, that it is important to implement an ability to associate these classes to each other so they can be used together in templates where PRNG type itself is passed as template parameter. Standard solution for this problem is template specialization. First, we declare template class representing a family of specific type traits:

template <class R> class rng_traits;

And then, for each PRNG class, we specialize this template:

class xorshift128

class rng_traits<xorshift128>

The resulting set of specializations can be later used either with fixed PRNG types:

void do_something()
  using traits = rng_traits<xorshift128>;

  xorshift128 rng;

  auto s = traits::get_state(rng);


or even with some type which is currently unknown:

template <class R>
void do_something()
  using traits = rng_traits<R>;

  R rng;

  auto s = traits::get_state(rng);


Note that using type traits, we can extend the functionality of some class multiple times in different ways and do it non-intrusively as long as we can manipulate the objects of this class via its public interface. In our case, however, I decided to allow traits to access the internals of PRNG classes making their public interface smaller. Also, this approach is very similar to the notion of type classes available in Haskell.

Demo code implemented for this article simply puts both the PRNG class and its traits to the same header file but in real life, it would possibly be better to use separate files and namespace conventions to isolate these internals from client code.

The code implementing exploratory seeding makes following assumptions regarding PRNG traits:

  • PRNG trait defines some vector-like container type state_t representing the PRNG state. The size of this container is fixed and available at the compile time via anonymous enumeration with single item named N.

  • Type of state vector elements could be arbitrary but with behavior resembling numbers (namely, integer constants 0 and 1 are implicitly converted to corresponding elements of that type, operations of multiplication and in-place addition work in a natural way).

    Our demo uses state vectors of two types: standard uint32_t and user-defined int_mod<M>, implementing the required subset of operations over finite ring of integers modulo \(M\).

  • State vectors can be read from the PRNG instance with get_state() and written there with set_state().

  • No assumption is made regarding the initial value of the state_t instance. Therefore, and additional function zero_state() is needed to create zero state vector used in exploratory seeding procedures.

After preparatory work is done, we are ready to implement the goal of our journey: the generic function makeT() calculating transition matrix of arbitrary linear PRNG. Note that our implementation is completely abstract and could be used for any size of the PRNG state and even for any type of state items. This level of flexibility could be achieved by moving all PRNG-specific implementation details to corresponding trait classes.

template<class R>
class tr_utils
  using traits = rng_traits<R>;
  enum { N = traits::N };
  using state_t = typename traits::state_t ;
  using T_t = std::array<std::array<typename state_t::value_type, N>, N> ;
  static T_t make_T()
    T_t T;

    R rng;

    for(size_t i = 0; i < N; i++)
      state_t st = traits::zero_state();

      st[i] = 1;

      traits::set_state(rng, st);


      st = traits::get_state(rng);

      for(size_t j = 0; j < N; j++)
        T[j][i] = st[j];

    return T;

Note that makeT() has been made a static member of the tr_utils template class. This class also implements some other functions used together: matmul() which simply multiplies the transition matrix by state vector effectively advancing the PRNG one step forward, and print_T() which outputs transition matrix and can be useful for debugging. Putting all those functions to the same outer class allows us to introduce common type aliases making code more clear.

Finally, let's consider, the only .cc file in the demo code. It contains three unit tests which could be used both to check that everything works properly and, additionally, to provide an example of using classes and functions described above.

TEST(ExplSeed, XorShift128)
  using traits = rng_traits<xorshift128>;
  using utils = tr_utils<xorshift128>;
  xorshift128 rng;

  auto T = utils::make_T();
  auto s = traits::get_state(rng);

  for(int i = 0 ; i < num_steps; i++)

    s = utils::matmul(T, s);

    ASSERT_EQ(s, traits::get_state(rng))
       << "Failed at step " << i;

All tests have exactly the same structure except for the used PRNG type. Could't we probably add one more template function to reduce source code size even further? I wouldn't do that because in this case, all line numbers shown in error messages will be the same for all tests and refer to that helper function rather than to some particular failed test.

The test procedure itself is quite trivial: we create an instance of some PRNG type, copy its state and then perform num_steps updates of this state using both transition matrix calculated for that PRNG type and its member function step_fwd(). After each step, the internal state of the generator is extracted and compared with calculated state vector reporting an error if they are different.


In this article, we have discussed one more way to create a transition matrix for some linear PRNG. Unlike other approaches considered in my previous articles, this one is simpler and could be, to some extent, used in a ``black box'' manner, i.e., without full knowledge of the PRNG internals. Some information, however, still be needed, such as dimensionality of the state vector and the nature of its components (are they real or integer, which modulo is used for integer arithmetics, and so on).

The implementation part of this article could also be used as simple use case of C++ programming technique known as type traits which is more suitable for academic purposes rather than referring to the source code of production grade libraries, such as Boost.


  • [1] Donald E. Knuth. The Art of Computer Programming, Volume 2 (3rd Ed.): Seminumerical Algorithms. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1997.
  • [2] P. L’Ecuyer. Uniform random number generation. Annals of Operations Research, 53:77–120, 1994.
  • [3] George Marsaglia. Xorshift RNGs. Journal of Statistical Software, Articles, 8(14):1–6, 2003. (Available online.)
  • [4] William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. Numerical Recipes in C (2nd Ed.): The Art of Scientific Computing. Cambridge University Press, USA, 1992.


This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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Comments and Discussions

GeneralMy vote of 5 Pin
Ștefan-Mihai MOGA6-Jan-24 21:01
professionalȘtefan-Mihai MOGA6-Jan-24 21:01 
GeneralRe: My vote of 5 Pin
Member 42018137-Jan-24 4:06
Member 42018137-Jan-24 4:06 
Thank you very much!

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