statistically-likely-usernames provides a collection of wordlists designed to generate statistically common usernames, crucial for username enumeration and password testing. These lists are based on data from various sources, including US census data and Facebook name data, emphasizing the statistical likelihood of common username patterns. The project addresses the need for efficient password guessing by focusing on frequently used username formats, optimizing attack success rates and minimizing account lockouts. By starting with statistically probable usernames, security professionals and penetration testers can achieve a higher success rate from their password attacks.
This project stands out due to its extensive collection of interleaved username lists, maximizing coverage across diverse formats with a single set of guesses. The inclusion of tools like DOBer for generating statistically likely dates of birth further enhances its utility. The project's documented origin and testing history, including live penetration tests, provide confidence in its effectiveness. Its adaptability with base-lists allows for tailoring usernames to specific organizational naming conventions, making it highly versatile.
- awesome-mix-vol1.txt: Interleaved common username formats with service and test accounts for broad coverage.
- awesome-mix-vol2.txt: Continuation of vol1, offering more interleaved entries for deeper coverage.
- DOBer: Python and PowerShell scripts for generating statistically likely dates of birth for password reset scenarios.
The project has been actively maintained since 2016, demonstrating sustained interest and utility. The presence of numerous downloads (1294 stars, 158 forks) indicates a reasonable level of adoption by the security community. The README is comprehensive, with detailed documentation of the various lists and tools provided. Active development is indicated by the last commit date of 2026-02-16.
This project is beneficial for penetration testers, security auditors, and anyone conducting username enumeration exercises. It provides a valuable resource for efficiently generating realistic usernames, addressing common password vulnerabilities and facilitating successful security assessments. The project simplifies password attacks and enhances security audits through statistically-informed username generation, surpassing manual approaches and less comprehensive lists.