Password Hashing ======================================== Storing passwords for user authentication purposes in plaintext is the simplest but least secure method; when an attacker compromises the database in which the passwords are stored, they immediately gain access to all of them. Often passwords are reused among multiple services or machines, meaning once a password to a single service is known an attacker has a substantial head start on attacking other machines. The general approach is to store, instead of the password, the output of a one way function of the password. Upon receiving an authentication request, the authenticator can recompute the one way function and compare the value just computed with the one that was stored. If they match, then the authentication request succeeds. But when an attacker gains access to the database, they only have the output of the one way function, not the original password. Common hash functions such as SHA-256 are one way, but used alone they have problems for this purpose. What an attacker can do, upon gaining access to such a stored password database, is hash common dictionary words and other possible passwords, storing them in a list. Then he can search through his list; if a stored hash and an entry in his list match, then he has found the password. Even worse, this can happen *offline*: an attacker can begin hashing common passwords days, months, or years before ever gaining access to the database. In addition, if two users choose the same password, the one way function output will be the same for both of them, which will be visible upon inspection of the database. There are two solutions to these problems: salting and iteration. Salting refers to including, along with the password, a randomly chosen value which perturbs the one way function. Salting can reduce the effectivness of offline dictionary generation, because for each potential password, an attacker would have to compute the one way function output for all possible salts. It also prevents the same password from producing the same output, as long as the salts do not collide. Choosing n-bit salts randomly, salt collisions become likely only after about 2\ :sup:\ `(n/2)` salts have been generated. Choosing a large salt (say 80 to 128 bits) ensures this is very unlikely. Note that in password hashing salt collisions are unfortunate, but not fatal - it simply allows the attacker to attack those two passwords in parallel easier than they would otherwise be able to. The other approach, iteration, refers to the general technique of forcing multiple one way function evaluations when computing the output, to slow down the operation. For instance if hashing a single password requires running SHA-256 100,000 times instead of just once, that will slow down user authentication by a factor of 100,000, but user authentication happens quite rarely, and usually there are more expensive operations that need to occur anyway (network and database I/O, etc). On the other hand, an attacker who is attempting to break a database full of stolen password hashes will be seriously inconvenienced by a factor of 100,000 slowdown; they will be able to only test at a rate of .0001% of what they would without iterations (or, equivalently, will require 100,000 times as many zombie botnet hosts). Memory usage while checking a password is also a consideration; if the computation requires using a certain minimum amount of memory, then an attacker can become memory-bound, which may in particular make customized cracking hardware more expensive. Some password hashing designs, such as scrypt, explicitly attempt to provide this. The bcrypt approach requires over 4 KiB of RAM (for the Blowfish key schedule) and may also make some hardware attacks more expensive. Botan provides two techniques for password hashing, bcrypt and passhash9. .. _bcrypt: Bcrypt Password Hashing ---------------------------------------- :wikipedia:`Bcrypt` is a password hashing scheme originally designed for use in OpenBSD, but numerous other implementations exist. It is made available by including ``bcrypt.h``. Bcrypt provides outputs that look like this:: "$2a$12$7KIYdyv8Bp32WAvc.7YvI.wvRlyVn0HP/EhPmmOyMQA4YKxINO0p2" .. cpp:function:: std::string generate_bcrypt(const std::string& password, \ RandomNumberGenerator& rng, u16bit work_factor = 10) Takes the password to hash, a rng, and a work factor. Higher values increase the amount of time the algorithm runs, increasing the cost of cracking attempts. The resulting hash is returned as a string. .. cpp:function:: bool check_bcrypt(const std::string& password, \ const std::string& hash) Takes a password and a bcrypt output and returns true if the password is the same as the one that was used to generate the bcrypt hash. Here is an example of using bcrypt: .. literalinclude:: ../src/apps/bcrypt.cpp .. _passhash9: Passhash9 ---------------------------------------- Botan also provides a password hashing technique called passhash9, in ``passhash9.h``, which is based on PBKDF2. Its outputs look like:: "$9$AAAKxwMGNPSdPkOKJS07Xutm3+1Cr3ytmbnkjO6LjHzCMcMQXvcT" .. cpp:function:: std::string generate_passhash9(const std::string& password, \ RandomNumberGenerator& rng, u16bit work_factor = 10, byte alg_id = 1) Functions much like ``generate_bcrypt``. The last parameter, ``alg_id``, specifies which PRF to use. Currently defined values are 0: HMAC(SHA-1), 1: HMAC(SHA-256), 2: CMAC(Blowfish), 3: HMAC(SHA-384), 4: HMAC(SHA-512) Currently, this performs 10000 * ``work_factor`` PBKDF2 iterations, using 96 bits of salt taken from ``rng``. The iteration count is encoded as a 16-bit integer and is multiplied by 10000. .. cpp:function:: bool check_passhash9(const std::string& password, \ const std::string& hash) Functions much like ``check_bcrypt``