SecLists-Express is the security tester's companion. It's a collection of multiple types of lists used during security assessments, collected in one place. List types include usernames, passwords, URLs, sensitive data patterns, fuzzing payloads, web shells, and many more.
* `feat(wordlist): Added Common German Passwords`: Add a wordlist for common German passwords and top 1000, 10,000, 100,000 and 1,000,000 lists. The `German_common-password-list.txt` contains ~1.8 million passwords.
* `feat(docs): Added Documentation for Common German Passwords`: Add wordlist files to `Passwords/Common-Credentials/Language-Specific/README.md`
Source
The password list is based on (more or less) public combo lists from multiple leak forums. Over the past year I got multiple German based email:password-lists based on leaked data. All lists together containing round about 2.5 million German passwords. I wrote a small helper tool to parse these lists and remove duplicates. The resulting list is added as German_common-password-list.txt.
Additional context & Purpose of pull request
Language-specific password lists are vital in pentesting, as they improve the chances of identifying weak passwords based on linguistic patterns. These lists reflect cultural and regional preferences, making security assessments more accurate. They enhance the effectiveness of password attacks by aligning with users' likely choices.
Describe the added commits
Source
The password list is based on (more or less) public combo lists from multiple leak forums. Over the past year I got multiple German based
email:password-lists based on leaked data. All lists together containing round about 2.5 million German passwords. I wrote a small helper tool to parse these lists and remove duplicates. The resulting list is added asGerman_common-password-list.txt.Additional context & Purpose of pull request
Language-specific password lists are vital in pentesting, as they improve the chances of identifying weak passwords based on linguistic patterns. These lists reflect cultural and regional preferences, making security assessments more accurate. They enhance the effectiveness of password attacks by aligning with users' likely choices.