.. _random_number_generators:

Random Number Generators
========================================

The base class ``RandomNumberGenerator`` is in the header ``botan/rng.h``.

The major interfaces are

.. cpp:function:: void RandomNumberGenerator::randomize(uint8_t* output_array, size_t length)

  Places *length* random bytes into the provided buffer.

.. cpp:function:: void RandomNumberGenerator::add_entropy(const uint8_t* data, size_t length)

  Incorporates provided data into the state of the PRNG, if at all
  possible.  This works for most RNG types, including the system and
  TPM RNGs. But if the RNG doesn't support this operation, the data is
  dropped, no error is indicated.

.. cpp:function:: void RandomNumberGenerator::randomize_with_input(uint8_t* data, size_t length, \
    const uint8_t* ad, size_t ad_len)

  Like randomize, but first incorporates the additional input field
  into the state of the RNG. The additional input could be anything which
  parameterizes this request. Not all RNG types accept additional inputs.

.. cpp:function:: void RandomNumberGenerator::randomize_with_ts_input(uint8_t* data, size_t length)

  Creates a buffer with some timestamp values and calls ``randomize_with_input``

.. cpp:function:: uint8_t RandomNumberGenerator::next_byte()

  Generates a single random byte and returns it. Note that calling this
  function several times is much slower than calling ``randomize`` once
  to produce multiple bytes at a time.

RNG Types
----------------------------------------

The following RNG types are included

HMAC_DRBG
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

HMAC DRBG is a random number generator designed by NIST and specified
in SP 800-90A. It seems to be the most conservative generator of the
NIST approved options.

It can be instantiated with any HMAC but is typically used with
SHA-256, SHA-384, or SHA-512, as these are the hash functions approved
for this use by NIST.

System_RNG
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In ``system_rng.h``, objects of ``System_RNG`` reference a single
(process global) reference to the system PRNG (such as
``/dev/urandom`` or ``CryptGenRandom``).

You can also use the function ``system_rng()`` which returns a
reference to the global handle to the system RNG.

AutoSeeded_RNG
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

AutoSeeded_RNG is type naming a 'best available' userspace PRNG. The
exact definition of this has changed over time and may change in the
future, fortunately there is no compatability concerns when changing
any RNG since the only expectation is it produces bits
indistinguishable from random.

Note well: like most other classes in Botan, it is not safe to share
an instance of ``AutoSeeded_RNG`` among multiple threads without
serialization.

The current version uses the HMAC_DRBG with SHA-384 or SHA-256. The
initial seed is generated either by the system PRNG (if available) or
a default set of entropy sources. These are also used for periodic
reseeding of the RNG state.

ChaCha_RNG
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This is a very fast userspace PRNG based on ChaCha20 and HMAC(SHA-256). The key
for ChaCha is derived by hashing entropy inputs with HMAC. Then the ChaCha
keystream generator is run, first to generate the new HMAC key (used for any
future entropy additions), then the desired RNG outputs.

This RNG composes two primitives thought to be secure (ChaCha and HMAC) in a
simple and well studied way (the extract-then-expand paradigm), but is still an
ad-hoc and non-standard construction. It is included because it is roughly 20x
faster then HMAC_DRBG, and certain applications need access to a very fast RNG.

RDRAND_RNG
^^^^^^^^^^^^^^^^^

This RNG type directly calls the x86 ``rdrand`` instruction. If the instruction
is not available it will throw at runtime, you can check beforehand by calling
``Botan::CPUID::has_rdrand()``.

TPM_RNG
^^^^^^^^^^^^^^^^^

This RNG type allows using the RNG exported from a TPM chip.

PKCS11_RNG
^^^^^^^^^^^^^^^^^

This RNG type allows using the RNG exported from a hardware token accessed via PKCS11.

Entropy Sources
---------------------------------

An ``EntropySource`` is an abstract representation of some method of
gather "real" entropy. This tends to be very system dependent. The
*only* way you should use an ``EntropySource`` is to pass it to a PRNG
that will extract entropy from it -- never use the output directly for
any kind of key or nonce generation!

``EntropySource`` has a pair of functions for getting entropy from
some external source, called ``fast_poll`` and ``slow_poll``. These
pass a buffer of bytes to be written; the functions then return how
many bytes of entropy were gathered.

Note for writers of ``EntropySource`` subclasses: it isn't necessary
to use any kind of cryptographic hash on your output. The data
produced by an EntropySource is only used by an application after it
has been hashed by the ``RandomNumberGenerator`` that asked for the
entropy, thus any hashing you do will be wasteful of both CPU cycles
and entropy.

Fork Safety
---------------------------------

On Unix platforms, the ``fork()`` and ``clone()`` system calls can
be used to spawn a new child process. Fork safety ensures that the
child process doesn't see the same output of random bytes as the
parent process. Botan tries to ensure fork safety by feeding the
process ID into the internal state of the random generator and by
automatically reseeding the random generator if the process ID
changed between two requests of random bytes. However, this does
not protect against PID wrap around. The process ID is usually
implemented as a 16 bit integer. In this scenario, a process will
spawn a new child process, which exits the parent process and
spawns a new child process himself. If the PID wrapped around, the
second child process may get assigned the process ID of it's 
grandparent and the fork safety can not be ensured.

Therefore, it is strongly recommended to explicitly reseed any
userspace random generators after forking a new process. If this is
not possible in your application, prefer using the system PRNG
instead.
