[][src]Module rand::prng

Pseudo-random number generators.

Pseudo-random number generators are algorithms to produce apparently random numbers deterministically, and usually fairly quickly. See the documentation of the rngs module for some introduction to PRNGs.

As mentioned there, PRNGs fall in two broad categories:

  • basic PRNGs, primarily designed for simulations
  • CSPRNGs, primarily designed for cryptography

In simple terms, the basic PRNGs are often predictable; CSPRNGs should not be predictable when used correctly.

Contents of this documentation:

  1. The generators
  2. Performance and size
  3. Quality and cycle length
  4. Security
  5. Extra features
  6. Further reading

The generators

Basic pseudo-random number generators (PRNGs)

The goal of regular, non-cryptographic PRNGs is usually to find a good balance between simplicity, quality, memory usage and performance. These algorithms are very important to Monte Carlo simulations, and also suitable for several other problems such as randomized algorithms and games (except where there is a risk of players predicting the next output value from previous values, in which case a CSPRNG should be used).

Currently Rand provides only one PRNG, and not a very good one at that:

name full name performance memory quality period features
XorShiftRng Xorshift 32/128 ★★★☆☆ 16 bytes ★☆☆☆☆ u32 * 2128 - 1

Cryptographically secure pseudo-random number generators (CSPRNGs)

CSPRNGs have much higher requirements than basic PRNGs. The primary consideration is security. Performance and simplicity are also important, but in general CSPRNGs are more complex and slower than regular PRNGs. Quality is no longer a concern, as it is a requirement for a CSPRNG that the output is basically indistinguishable from true randomness since any bias or correlation makes the output more predictable.

There is a close relationship between CSPRNGs and cryptographic ciphers. Any block cipher can be turned into a CSPRNG by encrypting a counter. Stream ciphers are basically a CSPRNG and a combining operation, usually XOR. This means that we can easily use any stream cipher as a CSPRNG.

Rand currently provides two trustworthy CSPRNGs and two CSPRNG-like PRNGs:

name full name performance initialization memory predictability forward secrecy
ChaChaRng ChaCha20 ★☆☆☆☆ fast 136 bytes secure no
Hc128Rng HC-128 ★★☆☆☆ slow 4176 bytes secure no
IsaacRng ISAAC ★★☆☆☆ slow 2072 bytes unknown unknown
Isaac64Rng ISAAC-64 ★★☆☆☆ slow 4136 bytes unknown unknown

It should be noted that the ISAAC generators are only included for historical reasons, they have been with the Rust language since the very beginning. They have good quality output and no attacks are known, but have received little attention from cryptography experts.


First it has to be said most PRNGs are very fast, and will rarely be a performance bottleneck.

Performance of basic PRNGs is a bit of a subtle thing. It depends a lot on the CPU architecture (32 vs. 64 bits), inlining, and also on the number of available registers. This often causes the performance to be affected by surrounding code due to inlining and other usage of registers.

When choosing a PRNG for performance it is important to benchmark your own application due to interactions between PRNGs and surrounding code and dependence on the CPU architecture as well as the impact of the size of data requested. Because of all this, we do not include performance numbers here but merely a qualitative rating.

CSPRNGs are a little different in that they typically generate a block of output in a cache, and pull outputs from the cache. This allows them to have good amortised performance, and reduces or completely removes the influence of surrounding code on the CSPRNG performance.

Worst-case performance

Because CSPRNGs usually produce a block of values into a cache, they have poor worst case performance (in contrast to basic PRNGs, where the performance is usually quite regular).

State size

Simple PRNGs often use very little memory, commonly only a few words, where a word is usually either u32 or u64. This is not true for all non-cryptographic PRNGs however, for example the historically popular Mersenne Twister MT19937 algorithm requires 2.5 kB of state.

CSPRNGs typically require more memory; since the seed size is recommended to be at least 192 bits and some more may be required for the algorithm, 256 bits would be approximately the minimum secure size. In practice, CSPRNGs tend to use quite a bit more, ChaChaRng is relatively small with 136 bytes of state.

Initialization time

The time required to initialize new generators varies significantly. Many simple PRNGs and even some cryptographic ones (including ChaChaRng) only need to copy the seed value and some constants into their state, and thus can be constructed very quickly. In contrast, CSPRNGs with large state require an expensive key-expansion.


Many basic PRNGs are not much more than a couple of bitwise and arithmetic operations. Their simplicity gives good performance, but also means there are small regularities hidden in the generated random number stream.

How much do those hidden regularities matter? That is hard to say, and depends on how the RNG gets used. If there happen to be correlations between the random numbers and the algorithm they are used in, the results can be wrong or misleading.

A random number generator can be considered good if it gives the correct results in as many applications as possible. The quality of PRNG algorithms can be evaluated to some extend analytically, to determine the cycle length and to rule out some correlations. Then there are empirical test suites designed to test how well a PRNG performs on a wide range of possible uses, the latest and most complete of which are TestU01 and PractRand.

CSPRNGs tend to be more complex, and have an explicit requirement to be unpredictable. This implies there must be no obvious correlations between output values.

Quality stars:

PRNGs with 3 stars or more should be good enough for any purpose. 1 or 2 stars may be good enough for typical apps and games, but do not work well with all algorithms.


The period or cycle length of a PRNG is the number of values that can be generated after which it starts repeating the same random number stream. Many PRNGs have a fixed-size period, but for some only an expected average cycle length can be given, where the exact length depends on the seed.

On today's hardware, even a fast RNG with a cycle length of only 264 can be used for centuries before cycling. Yet we recommend a period of 2128 or more, which most modern PRNGs satisfy. Alternatively a PRNG with shorter period but support for multiple streams may be chosen. There are two reasons for this, as follows.

If we see the entire period of an RNG as one long random number stream, every independently seeded RNG returns a slice of that stream. When multiple RNG are seeded randomly, there is an increasingly large chance to end up with a partially overlapping slice of the stream.

If the period of the RNG is 2128, and an application consumes 248 values, it then takes about 232 random initializations to have a chance of 1 in a million to repeat part of an already used stream. This seems good enough for common usage of non-cryptographic generators, hence the recommendation of at least 2128. As an estimate, the chance of any overlap in a period of size p with n independent seeds and u values used per seed is approximately 1 - e^(-u * n^2 / (2 * p)).

Further, it is not recommended to use the full period of an RNG. Many PRNGs have a property called k-dimensional equidistribution, meaning that for values of some size (potentially larger than the output size), all possible values are produced the same number of times over the generator's period. This is not a property of true randomness. This is known as the generalized birthday problem, see the PCG paper for a good explanation. This results in a noticable bias on output after generating more values than the square root of the period (after 264 values for a period of 2128).



From the context of any PRNG, one can ask the question given some previous output from the PRNG, is it possible to predict the next output value? This is an important property in any situation where there might be an adversary.

Regular PRNGs tend to be predictable, although with varying difficulty. In some cases prediction is trivial, for example plain Xorshift outputs part of its state without mutation, and prediction is as simple as seeding a new Xorshift generator from four u32 outputs. Other generators, like PCG and truncated Xorshift* are harder to predict, but not outside the realm of common mathematics and a desktop PC.

The basic security that CSPRNGs must provide is the infeasibility to predict output. This requirement is formalized as the next-bit test; this is roughly stated as: given the first k bits of a random sequence, the sequence satisfies the next-bit test if there is no algorithm able to predict the next bit using reasonable computing power.

A further security that some CSPRNGs provide is forward secrecy: in the event that the CSPRNGs state is revealed at some point, it must be infeasible to reconstruct previous states or output. Note that many CSPRNGs do not have forward secrecy in their usual formulations.

As an outsider it is hard to get a good idea about the security of an algorithm. People in the field of cryptography spend a lot of effort analyzing existing designs, and what was once considered good may now turn out to be weaker. Generally it is best to use algorithms well-analyzed by experts, such as those recommended by NIST or ECRYPT.

State and seeding

It is worth noting that a CSPRNG's security relies absolutely on being seeded with a secure random key. Should the key be known or guessable, all output of the CSPRNG is easy to guess. This implies that the seed should come from a trusted source; usually either the OS or another CSPRNG. Our seeding helper trait, FromEntropy, and the source it uses (EntropyRng), should be secure. Additionally, ThreadRng is a CSPRNG, thus it is acceptable to seed from this (although for security applications fresh/external entropy should be preferred).

Further, it should be obvious that the internal state of a CSPRNG must be kept secret. With that in mind, our implementations do not provide direct access to most of their internal state, and Debug implementations do not print any internal state. This does not fully protect CSPRNG state; code within the same process may read this memory (and we allow cloning and serialisation of CSPRNGs for convenience). Further, a running process may be forked by the operating system, which may leave both processes with a copy of the same generator.

Not a crypto library

It should be emphasised that this is not a cryptography library; although Rand does take some measures to provide secure random numbers, it does not necessarily take all recommended measures. Further, cryptographic processes such as encryption and authentication are complex and must be implemented very carefully to avoid flaws and resist known attacks. It is therefore recommended to use specialized libraries where possible, for example openssl, ring and the RustCrypto libraries.

Extra features

Some PRNGs may provide extra features, like:

  • Support for multiple streams, which can help with parallel tasks.
  • The ability to jump or seek around in the random number stream; with large periood this can be used as an alternative to streams.

Further reading

There is quite a lot that can be said about PRNGs. The PCG paper is a very approachable explaining more concepts.

A good paper about RNG quality is "Good random number generators are (not so) easy to find" by P. Hellekalek.


pub use self::chacha::ChaChaRng;
pub use self::hc128::Hc128Rng;
pub use self::isaac::IsaacRng;
pub use self::isaac64::Isaac64Rng;



The ChaCha random number generator.


The HC-128 random number generator.


The ISAAC random number generator.


The ISAAC-64 random number generator.



An Xorshift random number generator.