Building The Perfect Wordlist

Update 2: I've updated the wordlist to version 2. 10MB more wordlist with an average of 1,2% better accuracy.


Update: To buy the IQ Wordlist, go to the bottom of the article.

When it comes to password cracking, the fastest and most effective method is using a wordlist. It is more flexible than rainbow tables and it is faster than brute force. A good wordlist is key to cracking a lot of hashes, and that is why it is worth spending some time on building one.

The Goal
All hashes can be cracked given enough time. Brute forcing 10 million hashes is probably not a good idea and it is going to take you quite some time to get all of them (read: millions of years), no matter if it is MD5 or SHA1. Best practice is to brute force passwords of small length and let the wordlist take the rest. In this blog post, our goal is to get at least 30% coverage in under 1 minute given 30000 hashes. It also has to live up to the following criteria:
  1. Target the English language
  2. Have words between 6 and 25 characters
  3. Contains no words with space (less than 0.3% of passwords contain a space)
  4. Contain no symbols-only words (less than 0.02% of passwords contain symbols only)
  5. Have no difference in capitalization. Rules take care of capitalization.
Efficiency
How long does it take to crack the password "Password" using different techniques? It has a length of 8 and contains upper and lower case characters. Let's use a fast algorithm like MD5.

Hash: dc647eb65e6711e155375218212b3964
Brute force on GPU:1 hour 15 minutes with 1236.6M h/s
Brute force on CPU: 4 days 2 hours with 292.3M h/s
Wordlist GPU: < 1 sec
Wordlist CPU: < 1 sec


Note that this is not the actual time it would take to crack the password, but the time it takes to traverse the full character sets. This was measured on a 4 core 3.4 Ghz i7 and a Geforce GTX 560 TI using Hashcat and BarsWF.

Resources
Building a wordlist is not for the faint-hearted. It requires some knowledge on good data sources, word frequency, basic algorithms, data manipulation and a powerful computer that can do the heavy lifting.

Existing Wordlists
There are tons of wordlists floating around on the Internet. One thing they have in common is that they are poorly made and filled with garbage. I've seen wordlists containing up to 35% garbage including non-printable characters, wrong encoding, binary data and more
That being said, there are good wordlists on the Internet too, and I see no reason why we should not include them in our own wordlist.

The Process
This is the battle plan:
  1. Find data sources
  2. Combine data sources
  3. Apply constraints
  4. Merge duplicates
  5. Sort
Step 1: Find Data Sources
There are some good wordlists over at Openwall. They also include a wordlist with John The Ripper that is pretty good. Cain and Able also has a good wordlist.

SkullSecurity has done some premium work on gathering the different wordlists and cleaning leaked passwords for inclusion in wordlists, So I would highly recommend that you also use his resources.

Then we have the Oxford University wordlists, some small ones from outpost9 and last, but not least, the PacketStormSecurity wordlists.

One can go to the extremes and download Wikipedia and include that as a data source. But beware, we are talking many gigabytes of data and it can potentially take a very long time to process.

Step 2: Combine Data
This step might sound trivial, but one of the goals is to have a wordlist without spaces. So in this step, we need to split by spaces so that "John Doe" becomes "John" and "Doe" on each separate line. Then we combine all the data into one big list.

Step 3: Apply Constrains
Remove words of a length less than 6 and larger than 25. Brute force will take care of everything below 6 and we do not target hashes of very long passwords. We also remove all numbers as brute force can do a better job when it comes to numbers.

Many wordlists contain non-printable characters, uncommon symbols, wrong encoding, binary data and more. We would like a clean and small wordlist, so it is essential to clean the wordlist. You can use grep to do this or utilize regular expressions if you are building your own tools.

Step 4: Merge Duplicates
There is a reason I'm not using the word 'remove' here. To get the most out of our wordlist, we need to keep track of the frequency of each word in the wordlist. All wordlists contain 'password' and leaks contain it many times. Keeping track of the frequency of 'password' is essential to step 5. where we sort the data according to the frequency.

Step 5: Sort
This is what makes our wordlist efficient. We sort the whole wordlist according to the frequency of each word and then sort it alphabetically for each frequency. Some algorithms used in cracking is a bit faster when the passwords are in alphabetical order, and we want every bit of performance advantage we can get.

The result of this kind of sorting is that we get a list with all the most common passwords first, and as the passwords starts to be more uncommon, they are sorted alphabetically instead.

Results
I combined a couple of small lists of leaked hashes. I cracked all the passwords made of digits from 1-12 characters in 3 minutes. Then I cracked all 1-5 character passwords with digits, symbols, upper and lower case in about 20 seconds. A total of 29348 hashes was left in the test hash list.

I did a few runs using popular wordslists on the internet to compare it against the IQ wordlist I made.

Run #1 - Uniq wordlist (334 MB)
Time: 19 sec.
Cracked: 8781 (29.9%)

Run #2 - Uniq wordlist with Best64 rules
Time: 25 sec.
Cracked: 10652 (36.2%)

Run #3 - Hatelist wordlist (2.64 GB)
Time: 32 sec.
Cracked: 6705 (22.8%)

Run #4 - Hatelist wordlist with Best64 rules
Time: 1 min, 7 sec.
Cracked: 8753 (29.8%)

Run #5 - IQ wordlist (406 MB)
Time: 11 sec.
Cracked: 10770 (36.7%)

Run #6 - IQ wordlist with Best64 rules
Time: 17 sec.
Cracked: 11618 (39.5%)

As we can see, the IQ wordlist is a clear winner in terms of speed and efficiency.

Notes
The hashes that were given are from sites of multiple languages and a high number of computer specialists. This is what I considered the worst case hashes.

Comments

Popular posts from this blog

.NET Compression Libraries Benchmark

The Ramer-Douglas-Peucker Polygon Simplification Algorithm

The Power of Wolfram Alpha - Now in a .NET API