Wordlists and Rules
Turn a good wordlist into a great one with mutation rules and target intel.
What you will learn
- -Locate and choose the right wordlist for a job
- -Build a target-specific custom list from public information
- -Apply rule-based mutations to multiply your coverage
Cracking is a guessing game, and a wordlist is your list of guesses. The quality of the list decides whether you crack a hash in seconds or never. Raw (every possible character combination) is hopeless past a few characters, so real attacks lean on human predictability instead.
rockyou and friends
The most famous wordlist is rockyou.txt, roughly 14 million real
leaked from a 2009 breach. It ships with Kali and lives under
/usr/share/wordlists/. Because it is made of passwords humans actually chose,
it cracks a shocking number of weak hashes on its own.
$ls /usr/share/wordlists/$gunzip /usr/share/wordlists/rockyou.txt.gz$wc -l /usr/share/wordlists/rockyou.txtBeyond rockyou, the SecLists project bundles curated lists by category: common passwords, default , and leaked corporate sets. Bigger is not always better. A focused list that matches your target beats a giant generic one.
Build a custom list from target intel
Passwords reflect the people who make them. A company named Acme in Denver will
have accounts using Acme2024, Denver!, and Broncos1. Tools like cewl
scrape a target website and turn its words into a candidate list.
$cewl -d 2 -m 5 -w custom.txt https://target.exampleRules: one word becomes hundreds
A raw word like password is rarely the actual password. People capitalize,
append years, and swap letters for symbols. Rule-based mutation applies these
transformations automatically, so one base word expands into many candidates:
password, Password, Password1, P@ssw0rd, password2024.
Hashcat ships rule files under /usr/share/hashcat/rules/. The best64.rule set
is the starting point: 64 high-value mutations that catch most human
habits without exploding your runtime.
A 100,000-word list run through best64.rule tests 6.4 million candidates, and
they are smarter candidates than 6.4 million random . Reach for rules
before you reach for a bigger raw list.
The fastest crack on an engagement almost always comes from a small custom list seeded with the target's own vocabulary (company name, products, local sports teams) run through a rule set. Generic rockyou is your fallback, not your opener.
Why apply a rule file like best64 instead of just using a larger wordlist?
Which tool scrapes a target website to build a wordlist from its own vocabulary?
cewl (Custom Word List generator) spiders a site and emits the words it finds, ready to feed into hashcat or john.