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.\" $FreeBSD: releng/10.1/share/man/man4/random.4 256381 2013-10-12 15:31:36Z markm $
.\"
.Dd October 12, 2013
.Dt RANDOM 4
.Os
.Sh NAME
.Nm random
.Nd the entropy device
.Sh SYNOPSIS
.Cd "device random"
.Sh DESCRIPTION
The
.Nm
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.
.Pp
The software generator will start in an
.Em unseeded
state, and will block reads until
it is (re)seeded.
This may cause trouble at system boot
when keys and the like
are generated from
/dev/random
so steps should be taken to ensure a
reseed as soon as possible.
The
.Xr sysctl 8
controlling the
.Em seeded
status (see below) may be used
if security is not an issue
or for convenience
during setup or development.
.Pp
This initial seeding
of random number generators
is a bootstrapping problem
that needs very careful attention.
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.
It is (or more accurately should be)
critically important that the
.Nm
device is seeded
before the first time it is used.
In the case where a dummy or "blocking-only"
device is used,
it is the responsibility
of the system architect
to ensure that no blocking reads
hold up critical processes.
.Pp
To see the current settings of the software
.Nm
device, use the command line:
.Pp
.Dl sysctl kern.random
.Pp
which results in something like:
.Bd -literal -offset indent
kern.random.adaptors: yarrow,dummy
kern.random.active_adaptor: yarrow
kern.random.yarrow.gengateinterval: 10
kern.random.yarrow.bins: 10
kern.random.yarrow.fastthresh: 96
kern.random.yarrow.slowthresh: 128
kern.random.yarrow.slowoverthresh: 2
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: 1
.Ed
.Pp
Other than
.Dl kern.random.adaptors
all settings are read/write.
.Pp
The
.Va kern.random.sys.seeded
variable indicates whether or not the
.Nm
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
as a result of entropy harvesting.
A reseed will set the value to 1 (non-blocking).
.Pp
The
.Va 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.
.Pp
The
.Va 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.
.Pp
The
.Va 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.
.Pp
The
.Va 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.
.Pp
The other variables are explained in the paper describing the
.Em Yarrow
algorithm at
.Pa http://www.schneier.com/yarrow.html .
.Pp
These variables are all limited
in terms of the values they may contain:
.Bl -tag -width "kern.random.yarrow.gengateinterval" -compact -offset indent
.It Va kern.random.yarrow.gengateinterval
.Bq 4..64
.It Va kern.random.yarrow.bins
.Bq 2..16
.It Va kern.random.yarrow.fastthresh
.Bq 64..256
.It Va kern.random.yarrow.slowthresh
.Bq 64..256
.It Va kern.random.yarrow.slowoverthresh
.Bq 1..5
.El
.Pp
Internal
.Xr sysctl 3
handlers force the above variables
into the stated ranges.
.Sh RANDOMNESS
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.
.Pp
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
.Fx ,
seeding for cryptographic random number generators is provided by the
.Nm
device,
which provides real randomness.
The
.Xr 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
.Xr RAND_bytes 3
and
.Xr RAND_add 3 .
Note that OpenSSL uses the
.Nm
device for seeding automatically.
.Pp
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.
.Pp
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
.Nm
device could be used for seeding here,
but in practice, smaller seeds are usually considered acceptable.
.Pp
.Fx
provides two families of functions which are considered
suitable for simulation.
The
.Xr random 3
family of functions provides a random integer
between 0 to
.if t 2\u\s731\s10\d\(mi1.
.if n (2**31)\(mi1.
The functions
.Xr srandom 3 ,
.Xr initstate 3
and
.Xr setstate 3
are provided for deterministically setting
the state of the generator and
the function
.Xr srandomdev 3
is provided for setting the state via the
.Nm
device.
The
.Xr drand48 3
family of functions are also provided,
which provide random floating point numbers in various ranges.
.Pp
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 cases.
.Pp
.Fx
does also provide the traditional
.Xr 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 discouraged.
.Sh FILES
.Bl -tag -width ".Pa /dev/random"
.It Pa /dev/random
.El
.Sh SEE ALSO
.Xr arc4random 3 ,
.Xr drand48 3 ,
.Xr rand 3 ,
.Xr RAND_add 3 ,
.Xr RAND_bytes 3 ,
.Xr random 3 ,
.Xr sysctl 8
.Sh HISTORY
A
.Nm
device appeared in
.Fx 2.2 .
The early version was taken from Theodore Ts'o's entropy driver for Linux.
The current software implementation,
introduced in
.Fx 5.0 ,
is a complete rewrite by
.An Mark R V Murray ,
and is an implementation of the
.Em Yarrow
algorithm by Bruce Schneier,
.Em et al .
Significant infrastructure work was done by Arthur Mesh.
.Pp
The author gratefully acknowledges
significant assistance from VIA Technologies, Inc.