.. _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(byte* output_array, size_t length) Places *length* random bytes into the provided buffer. .. cpp:function:: void RandomNumberGenerator::add_entropy(const byte* 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(byte* data, size_t length, \ const byte* 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. .. cpp:function:: void RandomNumberGenerator::randomize_with_ts_input(byte* data, size_t length) Creates a buffer with some timestamp values and calls ``randomize_with_input`` .. cpp:function:: byte 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 such an RNG. 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. 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 the random generator after forking a new process.