Passwords remain a persistent point of tension between usability and security. Controls designed to strengthen authentication often introduce complexity, which encourages users to rely on familiar patterns rather than genuinely unpredictable credentials. In practice, this frequently results in passwords derived from an organization’s own language.
Attackers have long recognized this behavioral pattern and continue to exploit it. Rather than relying on artificial intelligence or sophisticated guessing algorithms, many credential attacks begin with something far simpler: harvesting contextual language and converting it into highly targeted password guesses.
Tools such as Custom Word List generators (CeWL) make this process efficient and repeatable without introducing additional technical complexity, significantly improving success rates while reducing noise and detection risk.
This attacker behavior helps explain why NIST SP 800-63B explicitly advises against the use of context-specific words in passwords, including service names, usernames, and related derivatives. Enforcing that guidance, however, requires an understanding of how attackers assemble and operationalize these wordlists in real-world attacks.
This distinction matters because many defensive strategies still assume that password guessing relies on broad, generic datasets.
Where targeted wordlists really come from
CeWL is an open-source web crawler that extracts words from websites and compiles them into structured lists. It is included by default in widely used penetration testing distributions such as Kali Linux and Parrot OS, which lowers the barrier to entry for both attackers and defenders.
Attackers use CeWL to crawl an organization’s public-facing digital presence and collect terminology that reflects how that organization communicates externally.
This typically includes company service descriptions, internal phrasing surfaced in documentation, and industry-specific language that would not appear in generic password dictionaries.
The effectiveness of this approach lies not in novelty, but in relevance. The resulting wordlists closely mirror the vocabulary users already encounter in their day-to-day work and are therefore more likely to influence password construction.
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