rand.pod 6.1 KB

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  1. =pod
  2. =head1 NAME
  3. rand - pseudo-random number generator
  4. =head1 SYNOPSIS
  5. #include <openssl/rand.h>
  6. int RAND_set_rand_engine(ENGINE *engine);
  7. int RAND_bytes(unsigned char *buf, int num);
  8. int RAND_pseudo_bytes(unsigned char *buf, int num);
  9. void RAND_seed(const void *buf, int num);
  10. void RAND_add(const void *buf, int num, int entropy);
  11. int RAND_status(void);
  12. int RAND_load_file(const char *file, long max_bytes);
  13. int RAND_write_file(const char *file);
  14. const char *RAND_file_name(char *file, size_t num);
  15. int RAND_egd(const char *path);
  16. void RAND_set_rand_method(const RAND_METHOD *meth);
  17. const RAND_METHOD *RAND_get_rand_method(void);
  18. RAND_METHOD *RAND_SSLeay(void);
  19. void RAND_cleanup(void);
  20. /* For Win32 only */
  21. void RAND_screen(void);
  22. int RAND_event(UINT, WPARAM, LPARAM);
  23. =head1 DESCRIPTION
  24. Since the introduction of the ENGINE API, the recommended way of controlling
  25. default implementations is by using the ENGINE API functions. The default
  26. B<RAND_METHOD>, as set by RAND_set_rand_method() and returned by
  27. RAND_get_rand_method(), is only used if no ENGINE has been set as the default
  28. "rand" implementation. Hence, these two functions are no longer the recommened
  29. way to control defaults.
  30. If an alternative B<RAND_METHOD> implementation is being used (either set
  31. directly or as provided by an ENGINE module), then it is entirely responsible
  32. for the generation and management of a cryptographically secure PRNG stream. The
  33. mechanisms described below relate solely to the software PRNG implementation
  34. built in to OpenSSL and used by default.
  35. These functions implement a cryptographically secure pseudo-random
  36. number generator (PRNG). It is used by other library functions for
  37. example to generate random keys, and applications can use it when they
  38. need randomness.
  39. A cryptographic PRNG must be seeded with unpredictable data such as
  40. mouse movements or keys pressed at random by the user. This is
  41. described in L<RAND_add(3)|RAND_add(3)>. Its state can be saved in a seed file
  42. (see L<RAND_load_file(3)|RAND_load_file(3)>) to avoid having to go through the
  43. seeding process whenever the application is started.
  44. L<RAND_bytes(3)|RAND_bytes(3)> describes how to obtain random data from the
  45. PRNG.
  46. =head1 INTERNALS
  47. The RAND_SSLeay() method implements a PRNG based on a cryptographic
  48. hash function.
  49. The following description of its design is based on the SSLeay
  50. documentation:
  51. First up I will state the things I believe I need for a good RNG.
  52. =over 4
  53. =item 1
  54. A good hashing algorithm to mix things up and to convert the RNG 'state'
  55. to random numbers.
  56. =item 2
  57. An initial source of random 'state'.
  58. =item 3
  59. The state should be very large. If the RNG is being used to generate
  60. 4096 bit RSA keys, 2 2048 bit random strings are required (at a minimum).
  61. If your RNG state only has 128 bits, you are obviously limiting the
  62. search space to 128 bits, not 2048. I'm probably getting a little
  63. carried away on this last point but it does indicate that it may not be
  64. a bad idea to keep quite a lot of RNG state. It should be easier to
  65. break a cipher than guess the RNG seed data.
  66. =item 4
  67. Any RNG seed data should influence all subsequent random numbers
  68. generated. This implies that any random seed data entered will have
  69. an influence on all subsequent random numbers generated.
  70. =item 5
  71. When using data to seed the RNG state, the data used should not be
  72. extractable from the RNG state. I believe this should be a
  73. requirement because one possible source of 'secret' semi random
  74. data would be a private key or a password. This data must
  75. not be disclosed by either subsequent random numbers or a
  76. 'core' dump left by a program crash.
  77. =item 6
  78. Given the same initial 'state', 2 systems should deviate in their RNG state
  79. (and hence the random numbers generated) over time if at all possible.
  80. =item 7
  81. Given the random number output stream, it should not be possible to determine
  82. the RNG state or the next random number.
  83. =back
  84. The algorithm is as follows.
  85. There is global state made up of a 1023 byte buffer (the 'state'), a
  86. working hash value ('md'), and a counter ('count').
  87. Whenever seed data is added, it is inserted into the 'state' as
  88. follows.
  89. The input is chopped up into units of 20 bytes (or less for
  90. the last block). Each of these blocks is run through the hash
  91. function as follows: The data passed to the hash function
  92. is the current 'md', the same number of bytes from the 'state'
  93. (the location determined by in incremented looping index) as
  94. the current 'block', the new key data 'block', and 'count'
  95. (which is incremented after each use).
  96. The result of this is kept in 'md' and also xored into the
  97. 'state' at the same locations that were used as input into the
  98. hash function. I
  99. believe this system addresses points 1 (hash function; currently
  100. SHA-1), 3 (the 'state'), 4 (via the 'md'), 5 (by the use of a hash
  101. function and xor).
  102. When bytes are extracted from the RNG, the following process is used.
  103. For each group of 10 bytes (or less), we do the following:
  104. Input into the hash function the local 'md' (which is initialized from
  105. the global 'md' before any bytes are generated), the bytes that are to
  106. be overwritten by the random bytes, and bytes from the 'state'
  107. (incrementing looping index). From this digest output (which is kept
  108. in 'md'), the top (up to) 10 bytes are returned to the caller and the
  109. bottom 10 bytes are xored into the 'state'.
  110. Finally, after we have finished 'num' random bytes for the caller,
  111. 'count' (which is incremented) and the local and global 'md' are fed
  112. into the hash function and the results are kept in the global 'md'.
  113. I believe the above addressed points 1 (use of SHA-1), 6 (by hashing
  114. into the 'state' the 'old' data from the caller that is about to be
  115. overwritten) and 7 (by not using the 10 bytes given to the caller to
  116. update the 'state', but they are used to update 'md').
  117. So of the points raised, only 2 is not addressed (but see
  118. L<RAND_add(3)|RAND_add(3)>).
  119. =head1 SEE ALSO
  120. L<BN_rand(3)|BN_rand(3)>, L<RAND_add(3)|RAND_add(3)>,
  121. L<RAND_load_file(3)|RAND_load_file(3)>, L<RAND_egd(3)|RAND_egd(3)>,
  122. L<RAND_bytes(3)|RAND_bytes(3)>,
  123. L<RAND_set_rand_method(3)|RAND_set_rand_method(3)>,
  124. L<RAND_cleanup(3)|RAND_cleanup(3)>
  125. =cut