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random - the entropy device


See Also


.Cd "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.burst: 20
kern.random.sys.harvest.ethernet: 0
kern.random.sys.harvest.point_to_point: 0
kern.random.sys.harvest.interrupt: 0
kern.random.yarrow.gengateinterval: 10
kern.random.yarrow.bins: 10
kern.random.yarrow.fastthresh: 100
kern.random.yarrow.slowthresh: 160
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.burst variable instructs the kernel thread that processes the harvest queue to tsleep(9) briefly after that many events have been processed. This helps prevent the random device from being so compute-bound that it takes over all processing ability. A value of 0 (zero) is treated as infinity, and will only allow the kernel to pause if the queue is empty. Only values in the range [0..20] are accepted.

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 interrupts are not considered as an entropy source. Set the variable to 1 (one) if you wish to use them for entropy harvesting. All interrupt harvesting is setup by the individual device drivers.

The other variables are explained in the paper describing the Yarrow algorithm at http://www.counterpane.com/yarrow.html.

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 ranges.


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
.Fx , 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.

.Fx provides two families of functions which are considered suitable for simulation. The random(3) family of functions provides a random integer between 0 to 2310-1. 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 cases.

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
.Fx random device, which is based on the Yarrow system, should be of some help in this area.

.Fx 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 discouraged.




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


Created by Blin Media, 2008-2013