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     random — the entropy device


     device random


     The random device returns an endless supply of random bytes when read.
     It also accepts and reads data as any ordinary (and willing) file, but
     discards data written to it.  The device will probe for certain hardware
     entropy sources, and use these in preference to the fallback, which is a
     generator implemented in software.

     If the device is using the software generator, writing data to random
     would perturb the internal state.  This perturbation of the internal
     state is the only userland method of introducing extra entropy into the
     device.  If the writer has superuser privilege, then closing the device
     after writing will make the software generator reseed itself.  This can
     be used for extra security, as it immediately introduces any/all new
     entropy into the PRNG.  The hardware generators will generate sufficient
     quantities of entropy, and will therefore ignore user-supplied input.
     The software random device may be controlled with sysctl(8).

     To see the current settings of the software random device, use the
     command line:

           sysctl kern.random

     which results in something like:

           kern.random.sys.seeded: 1
           kern.random.sys.harvest.ethernet: 1
           kern.random.sys.harvest.point_to_point: 1
           kern.random.sys.harvest.interrupt: 1
           kern.random.sys.harvest.swi: 0
           kern.random.yarrow.gengateinterval: 10
           kern.random.yarrow.bins: 10
           kern.random.yarrow.fastthresh: 192
           kern.random.yarrow.slowthresh: 256
           kern.random.yarrow.slowoverthresh: 2

     (These would not be seen if a hardware generator is present.)

     All settings are read/write.

     The kern.random.sys.seeded variable indicates whether or not the random
     device is in an acceptably secure state as a result of reseeding.  If set
     to 0, the device will block (on read) until the next reseed (which can be
     from an explicit write, or as a result of entropy harvesting).  A reseed
     will set the value to 1 (non-blocking).

     The kern.random.sys.harvest.ethernet variable is used to select LAN
     traffic as an entropy source.  A 0 (zero) value means that LAN traffic is
     not considered as an entropy source.  Set the variable to 1 (one) if you
     wish to use LAN traffic for entropy harvesting.

     The kern.random.sys.harvest.point_to_point variable is used to select
     serial line traffic as an entropy source.  (Serial line traffic includes
     PPP, SLIP and all tun0 traffic.)  A 0 (zero) value means such traffic is
     not considered as an entropy source.  Set the variable to 1 (one) if you
     wish to use it for entropy harvesting.

     The kern.random.sys.harvest.interrupt variable is used to select hardware
     interrupts as an entropy source.  A 0 (zero) value means hardware
     interrupts are not considered as an entropy source.  Set the variable to
     1 (one) if you wish to use them for entropy harvesting.  All hardware
     interrupt harvesting is set up by the individual device drivers.

     The kern.random.sys.harvest.swi variable is used to select software
     interrupts as an entropy source.  A 0 (zero) value means software
     interrupts are not considered as an entropy source.  Set the variable to
     1 (one) if you wish to use them for entropy harvesting.

     The other variables are explained in the paper describing the Yarrow
     algorithm at

     These variables are all limited in terms of the values they may contain:
           kern.random.yarrow.gengateinterval  [4..64]
           kern.random.yarrow.bins             [2..16]
           kern.random.yarrow.fastthresh       [64..256]
           kern.random.yarrow.slowthresh       [64..256]
           kern.random.yarrow.slowoverthresh   [1..5]

     Internal sysctl(3) handlers force the above variables into the stated


     The use of randomness in the field of computing is a rather subtle issue
     because randomness means different things to different people.  Consider
     generating a password randomly, simulating a coin tossing experiment or
     choosing a random back-off period when a server does not respond.  Each
     of these tasks requires random numbers, but the random numbers in each
     case have different requirements.

     Generation of passwords, session keys and the like requires cryptographic
     randomness.  A cryptographic random number generator should be designed
     so that its output is difficult to guess, even if a lot of auxiliary
     information is known (such as when it was seeded, subsequent or previous
     output, and so on).  On FreeBSD, seeding for cryptographic random number
     generators is provided by the random device, which provides real
     randomness.  The arc4random(3) library call provides a pseudo-random
     sequence which is generally reckoned to be suitable for simple
     cryptographic use.  The OpenSSL library also provides functions for
     managing randomness via functions such as RAND_bytes(3) and RAND_add(3).
     Note that OpenSSL uses the random device for seeding automatically.

     Randomness for simulation is required in engineering or scientific
     software and games.  The first requirement of these applications is that
     the random numbers produced conform to some well-known, usually uniform,
     distribution.  The sequence of numbers should also appear numerically
     uncorrelated, as simulation often assumes independence of its random
     inputs.  Often it is desirable to reproduce the results of a simulation
     exactly, so that if the generator is seeded in the same way, it should
     produce the same results.  A peripheral concern for simulation is the
     speed of a random number generator.

     Another issue in simulation is the size of the state associated with the
     random number generator, and how frequently it repeats itself.  For
     example, a program which shuffles a pack of cards should have 52!
     possible outputs, which requires the random number generator to have 52!
     starting states.  This means the seed should have at least log_2(52!) ~
     226 bits of state if the program is to stand a chance of outputting all
     possible sequences, and the program needs some unbiased way of generating
     these bits.  Again, the random device could be used for seeding here, but
     in practice, smaller seeds are usually considered acceptable.

     FreeBSD provides two families of functions which are considered suitable
     for simulation.  The random(3) family of functions provides a random
     integer between 0 to (2**31)−1.  The functions srandom(3), initstate(3)
     and setstate(3) are provided for deterministically setting the state of
     the generator and the function srandomdev(3) is provided for setting the
     state via the random device.  The drand48(3) family of functions are also
     provided, which provide random floating point numbers in various ranges.

     Randomness that is used for collision avoidance (for example, in certain
     network protocols) has slightly different semantics again.  It is usually
     expected that the numbers will be uniform, as this produces the lowest
     chances of collision.  Here again, the seeding of the generator is very
     important, as it is required that different instances of the generator
     produce independent sequences.  However, the guessability or
     reproducibility of the sequence is unimportant, unlike the previous

     One final consideration for the seeding of random number generators is a
     bootstrapping problem.  In some cases, it may be difficult to find enough
     randomness to seed a random number generator until a system is fully
     operational, but the system requires random numbers to become fully
     operational.  There is no substitute for careful thought here, but the
     FreeBSD random device, which is based on the Yarrow system, should be of
     some help in this area.

     FreeBSD does also provide the traditional rand(3) library call, for
     compatibility purposes.  However, it is known to be poor for simulation
     and absolutely unsuitable for cryptographic purposes, so its use is




     arc4random(3), drand48(3), rand(3), RAND_add(3), RAND_bytes(3),
     random(3), sysctl(8)


     A random device appeared in FreeBSD 2.2.  The early version was taken
     from Theodore Ts'o's entropy driver for Linux.  The current software
     implementation, introduced in FreeBSD 5.0, is a complete rewrite by Mark
     R V Murray, and is an implementation of the Yarrow algorithm by Bruce
     Schneier, et al.  The only hardware implementation currently is for the
     VIA C3 Nehemiah (stepping 3 or greater) CPU.  More will be added in the

     The author gratefully acknowledges significant assistance from VIA
     Technologies, Inc.