xref: /freebsd/share/man/man4/random.4 (revision 2be1a816b9ff69588e55be0a84cbe2a31efc0f2f)
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24.\" $FreeBSD$
25.\"
26.Dd July 19, 2006
27.Dt RANDOM 4
28.Os
29.Sh NAME
30.Nm random
31.Nd the entropy device
32.Sh SYNOPSIS
33.Cd "device random"
34.Sh DESCRIPTION
35The
36.Nm
37device
38returns an endless supply of random bytes when read.
39It also accepts and reads data
40as any ordinary (and willing) file,
41but discards data written to it.
42The device will probe for
43certain hardware entropy sources,
44and use these in preference to the fallback,
45which is a generator implemented in software.
46.Pp
47If the device is using
48the software generator,
49writing data to
50.Nm
51would perturb the internal state.
52This perturbation of the internal state
53is the only userland method of introducing
54extra entropy into the device.
55If the writer has superuser privilege,
56then closing the device after writing
57will make the software generator reseed itself.
58This can be used for extra security,
59as it immediately introduces any/all new entropy
60into the PRNG.
61The hardware generators will generate
62sufficient quantities of entropy,
63and will therefore ignore user-supplied input.
64The software
65.Nm
66device may be controlled with
67.Xr sysctl 8 .
68.Pp
69To see the current settings of the software
70.Nm
71device, use the command line:
72.Pp
73.Dl sysctl kern.random
74.Pp
75which results in something like:
76.Pp
77.Bd -literal -offset indent
78kern.random.sys.seeded: 1
79kern.random.sys.harvest.ethernet: 1
80kern.random.sys.harvest.point_to_point: 1
81kern.random.sys.harvest.interrupt: 1
82kern.random.sys.harvest.swi: 0
83kern.random.yarrow.gengateinterval: 10
84kern.random.yarrow.bins: 10
85kern.random.yarrow.fastthresh: 192
86kern.random.yarrow.slowthresh: 256
87kern.random.yarrow.slowoverthresh: 2
88.Ed
89.Pp
90(These would not be seen if a
91hardware generator is present.)
92.Pp
93All settings are read/write.
94.Pp
95The
96.Va kern.random.sys.seeded
97variable indicates whether or not the
98.Nm
99device is in an acceptably secure state
100as a result of reseeding.
101If set to 0, the device will block (on read) until the next reseed
102(which can be from an explicit write,
103or as a result of entropy harvesting).
104A reseed will set the value to 1 (non-blocking).
105.Pp
106The
107.Va kern.random.sys.harvest.ethernet
108variable is used to select LAN traffic as an entropy source.
109A 0 (zero) value means that LAN traffic
110is not considered as an entropy source.
111Set the variable to 1 (one)
112if you wish to use LAN traffic for entropy harvesting.
113.Pp
114The
115.Va kern.random.sys.harvest.point_to_point
116variable is used to select serial line traffic as an entropy source.
117(Serial line traffic includes PPP, SLIP and all tun0 traffic.)
118A 0 (zero) value means such traffic
119is not considered as an entropy source.
120Set the variable to 1 (one)
121if you wish to use it for entropy harvesting.
122.Pp
123The
124.Va kern.random.sys.harvest.interrupt
125variable is used to select hardware interrupts
126as an entropy source.
127A 0 (zero) value means hardware interrupts
128are not considered as an entropy source.
129Set the variable to 1 (one)
130if you wish to use them for entropy harvesting.
131All hardware interrupt harvesting is set up by the
132individual device drivers.
133.Pp
134The
135.Va kern.random.sys.harvest.swi
136variable is used to select software interrupts
137as an entropy source.
138A 0 (zero) value means software interrupts
139are not considered as an entropy source.
140Set the variable to 1 (one)
141if you wish to use them for entropy harvesting.
142.Pp
143The other variables are explained in the paper describing the
144.Em Yarrow
145algorithm at
146.Pa http://www.counterpane.com/yarrow.html .
147.Pp
148These variables are all limited
149in terms of the values they may contain:
150.Bl -tag -width "kern.random.yarrow.gengateinterval" -compact -offset indent
151.It Va kern.random.yarrow.gengateinterval
152.Bq 4..64
153.It Va kern.random.yarrow.bins
154.Bq 2..16
155.It Va kern.random.yarrow.fastthresh
156.Bq 64..256
157.It Va kern.random.yarrow.slowthresh
158.Bq 64..256
159.It Va kern.random.yarrow.slowoverthresh
160.Bq 1..5
161.El
162.Pp
163Internal
164.Xr sysctl 3
165handlers force the above variables
166into the stated ranges.
167.Sh RANDOMNESS
168The use of randomness in the field of computing
169is a rather subtle issue because randomness means
170different things to different people.
171Consider generating a password randomly,
172simulating a coin tossing experiment or
173choosing a random back-off period when a server does not respond.
174Each of these tasks requires random numbers,
175but the random numbers in each case have different requirements.
176.Pp
177Generation of passwords, session keys and the like
178requires cryptographic randomness.
179A cryptographic random number generator should be designed
180so that its output is difficult to guess,
181even if a lot of auxiliary information is known
182(such as when it was seeded, subsequent or previous output, and so on).
183On
184.Fx ,
185seeding for cryptographic random number generators is provided by the
186.Nm
187device,
188which provides real randomness.
189The
190.Xr arc4random 3
191library call provides a pseudo-random sequence
192which is generally reckoned to be suitable for
193simple cryptographic use.
194The OpenSSL library also provides functions for managing randomness
195via functions such as
196.Xr RAND_bytes 3
197and
198.Xr RAND_add 3 .
199Note that OpenSSL uses the
200.Nm
201device for seeding automatically.
202.Pp
203Randomness for simulation is required in engineering or
204scientific software and games.
205The first requirement of these applications is
206that the random numbers produced conform to some well-known,
207usually uniform, distribution.
208The sequence of numbers should also appear numerically uncorrelated,
209as simulation often assumes independence of its random inputs.
210Often it is desirable to reproduce
211the results of a simulation exactly,
212so that if the generator is seeded in the same way,
213it should produce the same results.
214A peripheral concern for simulation is
215the speed of a random number generator.
216.Pp
217Another issue in simulation is
218the size of the state associated with the random number generator, and
219how frequently it repeats itself.
220For example,
221a program which shuffles a pack of cards should have 52!\& possible outputs,
222which requires the random number generator to have 52!\& starting states.
223This means the seed should have at least log_2(52!) ~ 226 bits of state
224if the program is to stand a chance of outputting all possible sequences,
225and the program needs some unbiased way of generating these bits.
226Again,
227the
228.Nm
229device could be used for seeding here,
230but in practice, smaller seeds are usually considered acceptable.
231.Pp
232.Fx
233provides two families of functions which are considered
234suitable for simulation.
235The
236.Xr random 3
237family of functions provides a random integer
238between 0 to
239.if t 2\u\s731\s10\d\(mi1.
240.if n (2**31)\(mi1.
241The functions
242.Xr srandom 3 ,
243.Xr initstate 3
244and
245.Xr setstate 3
246are provided for deterministically setting
247the state of the generator and
248the function
249.Xr srandomdev 3
250is provided for setting the state via the
251.Nm
252device.
253The
254.Xr drand48 3
255family of functions are also provided,
256which provide random floating point numbers in various ranges.
257.Pp
258Randomness that is used for collision avoidance
259(for example, in certain network protocols)
260has slightly different semantics again.
261It is usually expected that the numbers will be uniform,
262as this produces the lowest chances of collision.
263Here again,
264the seeding of the generator is very important,
265as it is required that different instances of
266the generator produce independent sequences.
267However, the guessability or reproducibility of the sequence is unimportant,
268unlike the previous cases.
269.Pp
270One final consideration for the seeding of random number generators
271is a bootstrapping problem.
272In some cases, it may be difficult to find enough randomness to
273seed a random number generator until a system is fully operational,
274but the system requires random numbers to become fully operational.
275There is no substitute for careful thought here,
276but the
277.Fx
278.Nm
279device,
280which is based on the Yarrow system,
281should be of some help in this area.
282.Pp
283.Fx
284does also provide the traditional
285.Xr rand 3
286library call,
287for compatibility purposes.
288However,
289it is known to be poor for simulation and
290absolutely unsuitable for cryptographic purposes,
291so its use is discouraged.
292.Sh FILES
293.Bl -tag -width ".Pa /dev/random"
294.It Pa /dev/random
295.El
296.Sh SEE ALSO
297.Xr arc4random 3 ,
298.Xr drand48 3 ,
299.Xr rand 3 ,
300.Xr RAND_add 3 ,
301.Xr RAND_bytes 3 ,
302.Xr random 3 ,
303.Xr sysctl 8
304.Sh HISTORY
305A
306.Nm
307device appeared in
308.Fx 2.2 .
309The early version was taken from Theodore Ts'o's entropy driver for Linux.
310The current software implementation,
311introduced in
312.Fx 5.0 ,
313is a complete rewrite by
314.An Mark R V Murray ,
315and is an implementation of the
316.Em Yarrow
317algorithm by Bruce Schneier,
318.Em et al .
319The only hardware implementation
320currently is for the
321.Tn VIA C3 Nehemiah
322(stepping 3 or greater)
323CPU.
324More will be added in the future.
325.Pp
326The author gratefully acknowledges
327significant assistance from VIA Technologies, Inc.
328