But then, as it got to the point were it was trying less and less likely passwords and therefore found matches more slowly, I decided to stop it and run a series of old dictionaries I had: from default common password lists (16KB of data) to words of every existing language (40MB of data). It was very efficient and found 500K more passwords in less than an hour, for a total of 1.4M passwords.

Even though my dictionaries were 10 years old and didn't contain newer words like "linkedin", it appeared that some cracking rules, by reversing strings or removing some vowels could guess new slang words from already cracked passwords.


And as I had just acquired 1.4M valid passwords, I believed that using these newly discovered passwords as a dictionary I could find more. It worked and the rules applied to the already cracked passwords produced 550K new ones. I ran a second iteration using the 550K passwords from the first iteration as a dictionary, and found 22K more. I iterated in this manner a total of ten times.