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Password guessing without AI: The Targeted Wordlist Approach - A new study highlights the growing threat of targeted wordlist attacks on passwords, where attackers use contextual language from an organization's website to build highly targeted password guesses. Learn how organizations can defend against these attacks and implement more resilient authentication strategies.
Attackers exploit familiar patterns in password construction by using targeted wordlists. Wordlists are generated from an organization's public-facing digital presence, including company descriptions and industry-specific language. The effectiveness of wordlist-based attacks lies in relevance, not novelty. Attackers use tools like Custom Word List generators (CeWL) to crawl websites and compile targeted lists. Wordlists can be generated efficiently and repeatedly without introducing additional technical complexity. The goal of wordlist-based attacks is to generate plausible password guesses through systematic modification of base terms. Many passwords constructed in this way satisfy standard complexity requirements, making them vulnerable to brute-force techniques. To defend against wordlist-based attacks, organizations should address password construction rather than complexity alone. Controls can include blocking context-derived passwords, preventing organization-specific language use, and requiring minimum length and complexity requirements.
Password Guessing without AI: The Targeted Wordlist Approach
Passwords have long been a contentious issue when it comes to balancing security and usability. Controls designed to strengthen authentication can introduce complexity, leading users to rely on familiar patterns rather than truly unpredictable credentials. In practice, this often results in passwords that are derived from an organization's own language.
Attackers have recognized this behavioral pattern and continue to exploit it through the use of targeted wordlists. Rather than relying on artificial intelligence or sophisticated guessing algorithms, many credential attacks begin with something much 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.
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.
For a healthcare organization, for example, a hospital, public-facing content may expose terms such as the name of the organization, references to its location, or the services or treatments it offers. These terms are rarely used as passwords in isolation but instead serve as a foundational candidate set that attackers systematically modify using common patterns such as numeric suffixes, capitalization, or appended symbols to generate plausible password guesses.
Once attackers obtain password hashes, often through third-party breaches or infostealer infections, tools such as Hashcat apply these mutation rules at scale. Millions of targeted candidates can be generated and tested efficiently against compromised data. The same wordlists can also be used against live authentication services, where attackers may rely on throttling, timing, or low-and-slow guessing techniques to reduce the likelihood of detection or account lockout.
A key challenge is that many passwords generated in this way satisfy standard complexity requirements. Specops analysis of more than six billion compromised passwords suggests that organizations continue to struggle with this distinction, even where awareness and training programs are in place. When passwords are constructed from familiar organizational language, added length or character variety does little to offset the reduced uncertainty introduced by highly contextual base terms.
A password such as HospitalName123! illustrates this problem more clearly. While it exceeds default Active Directory complexity requirements, it remains a weak choice within a healthcare environment. CeWL-derived wordlists readily identify organization names and abbreviations harvested from public-facing content, allowing attackers to arrive at plausible password variants through minimal and systematic modification.
Reducing exposure to wordlist-based attacks requires controls that address password construction rather than complexity alone. Block context-derived and known-compromised passwords and prevent users from creating passwords based on organization-specific language such as company and product names, internal project terms, industry vocabulary, and common attacker substitutions. Continuously scanning Active Directory against more than 5.4 billion known-compromised passwords can disrupt CeWL-style wordlist attacks and reduce the reuse of exposed credentials.
Enforcing minimum length and complexity requirements can also mitigate the issue. Require at least 15-character passphrases as they offer the best protection against brute-force techniques. Passphrases are the best way to get users to create strong, long passwords. Additionally, enable multi-factor authentication (MFA) to protect Windows Logon, VPNs, and RDP connections.
In conclusion, password guessing without AI relies heavily on targeted wordlists that attackers build by harvesting contextual language from an organization's public-facing digital presence. By understanding how these wordlists are constructed and the challenges they pose, organizations can take steps to defend against them and implement more resilient authentication strategies.
Related Information:
https://www.ethicalhackingnews.com/articles/Password-Guessing-without-AI-The-Targeted-Wordlist-Approach-ehn.shtml
Published: Mon Feb 9 12:09:56 2026 by llama3.2 3B Q4_K_M
