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Population based metaheuristic for password cracking. Siga(Simple genetic algorithm)

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siga(simple genetic algorithm)

This is a small experiment that focuses on leveraging population-based meta-heuristics for cracking passwords. Click here to go to the section on how it works. When cracking the rock_you password list for 5 minutes, it finds the following top 10 longest passwords

16 aaaaaaaaaaaaaaaa
15 laaaaaaaaaaaaaa
15 aaaaaaaaaaaaaaa
14 tristadanielle
14 rachelwilliams
14 princessalinda
14 ilovechris1234
14 aaaaaaaaaaaaaa
13 victormariana
13 sarahmitchell
After about 18 hours it finds complex passwords like the following:
21 123456789101112131415
20 12345678910987654321
20 12345678910111213141
20 12345678909876543210
20 12345678900987654321
19 tinkerbell123456789
19 tequieromuchogerald
19 tequieromuchoandres
19 paulaalejandrailove
19 password12345678910
19 9876543211234567089
It does find very long passwords (>60 chars) as well but those generally only consist of 1 maybe 2 characters.

What can it be used for?

  • Cracking md5 and sha1 passwords(for now)
  • Expanding smaller dictionaries into larger ones(see Example 8)
  • Bolt itself onto a 3rd party program like hashcat with named pipes(see Example 6)
  • Study genetic algorithms and structures in passwords


  • It is written in modern c++ and it uses c++11 and c++14 features, so you need a compiler that supports it.
  • You need to have Boost installed since program options and the random generator requires it. I would recommend Boost 1.6, since I have had problems with earlier versions when it comes to program options.
  • Openssl crypto library. In debian/ubuntu
    $ apt-get install libssl-dev

How to build

Assuming you have satisfied the above dependencies, you can just run the silly makefile. You can then call the binary ./siga -h to see the program options.

How to use

There are 3 important files: * data/training.txt: location of the words you want to crack reside. In md5mode you will put your hashes here. Currently it has the hashed myspace leaked list in it. * data/organism.txt: location of the starting organisms, assuming you dont want to start of with random organisms. Currently it has the top 2000 ngrams from the rockyou list. * data/cracked.txt: When a password is cracked, it goes here.

The location of these files can be changed by providing the right arguments to siga. Look at ./siga -h for more information.

Show me a testrun with the md5 hashed myspace leaked list


$ ./siga --md5_mode --organism_file=data/organism.txt
Then look at cracked.txt with
$ tail -f data/cracked.txt
to see which md5 hashes it has managed to crack from the myspace list.

Other Examples:

Example 1: Cracking md5 passwords starting with a random population of organisms.

Place the hashes in data/training.txt, then call the program with the following options:

$ ./siga --md5_mode
You can look at the current progress by opening another terminal and running
$ tail -f data/cracked.txt
Note that we omitted the program argument
because we want the simulation to start with a random population.

Example 2: Cracking md5 passwords starting with a random population of organisms and I want to set the initial population size to 200.

$ ./siga --md5_mode --init_population 200

Example 3: Cracking md5 passwords starting with a random population of organisms and I want to give the program hints as it progresses.

$ ./siga --md5_mode --interactive --verbose

The verbose option provides you with startup information as well as a prompt to enter the words.

Example 4: Cracking md5 passwords starting with a random population of organisms and I want to give the program hints as it progresses, but I want the hints to come from a file.

$ cat somehints| ./siga --md5_mode --interactive

Alternatively if you want to disable the random starting population and use words from the organism file exclusively, you can put your hints into the data/organisms.txt file then run

$ ./siga --md5_mode --organism_file=data/organisms.txt

Example 5: I want to bypass the overhead of md5 and do experiments with plaintext datasets.

Just remove the --md5_mode argument. Now data/training.txt can be plaintext passwords.

$ ./siga --interactive --verbose
The cracked passwords will appear in data/cracked.txt.

Example 6: I want this program to generate candidate passwords that I can use in hashcat.

Using named pipes we can create a feedback loop that pushes cracked passwords back into siga, so that siga can keep learning.

cd hashcat
mkfifo hashcat-2.00/pipe_in
mkfifo hashcat-2.00/pipe_out
Then in one terminal run
./hashcat-cli64.bin -m 0 -a 0 --segment-size=1 --outfile-format=2 --outfile=pipe_out hashes.txt pipe_in
In another terminal run
cat hashcat-2.00/pipe_out | ./siga --interactive --dump_candidates > hashcat-2.00/pipe_in
Now if we want to give siga hints we can just do this
echo some_hint > /proc/`pidof siga`/fd/0

Example 7: I want to crack md5 passwords but I want to stop and resume the simulation at a later time.

Run the program as usual, no additional arguments are required. ``` $ ./siga --md5_mode

siga gets killed here

In order to resume the simulation, all we need to do is append data/cracked.txt into data/organism.txt inorder to reintroduce the organisms(in the right order).

$ cat data/cracked.txt >> data/organism.txt

Now we can resume the simulation with the following
$ ./siga --md5mode --organismfile=data/organisms.txt ``` This works because the organisms are plain strings and the cracked.txt file stores them in the exact order they appear in the program during execution.

Example 8: I want to inflate(expand) an existing smaller dictionary into a larger dictionary.

Here is one way to do it:

Specify your dictionary as the training set, and dumpall candidate passwords from the simulation to stdout ``` $ ./siga --dumpcandidates --trainingfile=mysmalldictionary > mysuperlargedictionary ``

Let this run for a while. You can always check how many lines the program has generated by taking a word-count of your super large dictionary. Like so:
$ wc -l mysuperlarge_dictionary`

Once you are happy with its size you can stop the simulation. Due to the way siga works, there will be a few duplicates in our new dictionary, so we should sort it and remove any duplicates.

$ sort -u my_super_large_dictionary > my_super_large_deduplicated_dictionary
Now you have a super large deduplicated dictionary.

How it works

We start of with a small vector of random strings or strings from file. With each iteration, we mutate and crossover organisms from random positions until one of their children matches a password. We then push the matched child into the end of the container and pop the oldest organism from the front. Here is a simple image that communicates the essentials of the algorithm:


This way we have all the properties of a genetic algorithm, with the exception of a conventional fitness function, since the fitness is binary(password match or not). Only offspring that crack a password are allowed to enter the genepool. Additionally, pressure is applied to the population by each organism having a limited lifetime to propagate its genes due to the oldest organisms being popped from the front of the container when new ones enter the back. The above mentioned algorithm is quite effective at preserving high impact substrings that can explain a large number of passwords. As these high impact substring are exhausted, mutations of them or new novel substrings will emerge and start to dominate the gene-pool. Since only a single organism can find a specific solution before that solution is removed from the solution space, the solution space will shrink until only highly complex words remain in the solution space.

Some empirical experimentation shows that the distribution of candidate parents that produce viable offspring are non uniformly distributed in the gene-pool, even though the parents have been picked at random in a uniform fashion. Below are the graphs of the experiment.

parent 1

parent 2

Thus, to match our observations, the current algorithm finds the first parent in a uniform manner and the second parent by reversing an exponential distribution in order to approximate the observation. Choosing the second parent non-uniformly with a bias toward the end of the container provides two additional advantages. Firstly, newer organisms will be given the chance to produce offspring more often than older organisms, which provides sufficient selection pressure so that old organisms do not strictly need to be deleted from the gene-pool since they will lie dormant most of the time. Secondly, since we are not deleting older organisms, the overall diversity of the gene-pool is preserved while providing the chance for dormant organisms to reintroduce their genes in a novel way some time in the future.

For crossover there are 4 strategies: * partialinsert * fullinsert * partialsubstitute * fullsubstitute.

For partialinsert, we take a random substring from the lhs string and insert it into a random position in rhs string. The fullinsert strategy inserts the full lhs string into a random position in rhs string. In a similar fashion, partialsubstitute substitutes a random substring from lhs into a random position in rhs. Finally, fullsubstitute substitutes the full lhs string into a random position in rhs. By empirical experimentation, I found that mostly partialinsert and partialsubstitute is used by successful organisms, so the other 2 are disabled for now. We don't lose any functionality since the 2 activated strategies can fulfill the role of the disabled ones. The disabled ones are, in a sense, a subset of the activated ones.

Mutation of an organism is done in a similar fashion. Mutation strategies include * random insertion * random substitution * random deletion

The lhs string is a random string of length [1,5] and the rhs string is an existing organism we are mutating.

Since we have more than one crossover strategy and mutations strategy, all of them are chosen at random, on the fly, with a uniform distribution.

Future work:

Instead of having the distribution for parent 1 and 2 fixed, it might be useful to have a discrete distribution for both parents and have this discrete distribution updated according to where good candidate parents occur. This way, we try to approximate a distribution that wastes as little time considering bad candidate parents as possible. We know already that these distributions exist by empirical measures.

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