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Jim Lambert
Jim Lambert

Wordlist Download: Where to Find and How to Use the Most Popular Wordlists for Hacking, Cracking, and Testing

Wordlist Download: A Guide to Finding and Using Wordlists

A wordlist is a list of words, phrases, passwords, or other strings that are used for various purposes, such as password cracking, content discovery, language learning, etc. Wordlists can be obtained from different sources, such as online repositories, dictionaries, corpora, or generated by tools. In this article, I will show you some examples of wordlists and how to use them effectively.

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Types of wordlists

Wordlists can be classified into different types based on their content and format. Some common types are:

  • General vs. specific wordlists: General wordlists contain common words or strings that are likely to appear in any context, such as the most frequent words in a language or the most common passwords. Specific wordlists contain words or strings that are relevant to a particular domain or topic, such as technical terms, names, or slang.

  • Static vs. dynamic wordlists: Static wordlists are fixed and do not change over time. They are usually based on existing sources or data sets. Dynamic wordlists are updated or generated on the fly based on new information or user input. They are usually created by tools or algorithms.

Sources of wordlists

There are many sources of wordlists available online or offline. Some popular sources are:

  • Online repositories: These are websites or platforms that host and share wordlists created by users or communities. Some examples are GitHub, SecLists, and Probable-Wordlists. These repositories often contain a variety of wordlists for different purposes and languages.

  • Dictionaries: These are collections of words and their meanings, usually arranged alphabetically. They can be used as wordlists for language learning or vocabulary expansion. Some examples are Oxford Learner's Dictionaries, Cambridge Dictionary, and Merriam-Webster Dictionary. These dictionaries often provide additional information about the words, such as pronunciation, usage, synonyms, etc.

  • Corpora: These are large and structured sets of texts or speech that represent natural language use in a specific context or genre. They can be used as wordlists for linguistic analysis or text generation. Some examples are British National Corpus, Corpus of Contemporary American English, and SUBTLEX. These corpora often provide frequency data and other statistics about the words.

  • Tools: These are software applications or scripts that can generate or modify wordlists based on various criteria or inputs. Some examples are Hashcat, Mentalist, and CUPP. These tools often allow users to customize and optimize their wordlists for specific tasks or targets.

Examples of wordlists

Wordlists can be used for different purposes depending on the user's needs and goals. Here are some examples of wordlists for common scenarios:

Wordlists for password cracking

Password cracking is the process of recovering passwords from data that has been stored in or transmitted by a computer system. Wordlists are often used in password cracking to perform dictionary attacks, which try to guess passwords by using a list of possible candidates. Password cracking can be done for legitimate reasons, such as testing the security of a system or recovering forgotten passwords, or for malicious reasons, such as hacking into someone else's account or stealing sensitive information.

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To perform a successful dictionary attack, the word list should contain words or strings that are likely to be used as passwords by the target system or user. Some factors that can affect the choice of wordlist are:

  • The type of system or service: Different systems or services may have different password policies or requirements, such as length, complexity, or format. For example, some websites may require passwords to contain at least one uppercase letter, one lowercase letter, one digit, and one special character. Some systems or services may also have common or default passwords that can be easily guessed.

  • The language or culture of the user: Different languages or cultures may have different preferences or habits for choosing passwords, such as using words from their native language, personal names, dates, slang, etc. For example, some Spanish speakers may use words like "amor", "familia", or "futbol" as passwords. Some Chinese users may use numbers that sound like words, such as "520" for "I love you".

  • The level of sophistication of the user: Different users may have different levels of awareness or knowledge about password security and best practices, such as using unique and random passwords, avoiding common or predictable patterns, or changing passwords regularly. For example, some novice or careless users may use simple or weak passwords, such as "password", "123456", or "qwerty". Some advanced or cautious users may use complex or strong passwords, such as "g4H!2#n9", "Mj7@xQ3Z", or "pR8wT6yK".

Some examples of wordlists for password cracking are:





A list of 14 million passwords that were leaked from the social networking site RockYou in 2009. It contains many common and weak passwords, such as "123456", "iloveyou", or "princess".

SecLists Passwords

A collection of wordlists for password cracking curated by Daniel Miessler. It contains various types of wordlists, such as common passwords, default passwords, leaked passwords, keyboard patterns, etc.

Probable-Wordlists Top 207

A list of the top 207 most probable passwords based on frequency analysis and probability calculations. It contains many popular and easy-to-guess passwords, such as "password1", "abc123", or "letmein".

CUPP Common User Passwords Profiler

A tool that can generate wordlists based on personal information about the target user, such as name, date of birth, hobbies, pets, etc. It can create customized and realistic passwords that the user may use.

Hashcat Rule-based Attack

A method that can modify wordlists by applying rules that mimic common password transformations, such as appending numbers, changing cases, replacing letters with symbols, etc. It can increase the chances of cracking passwords that are based on words with slight variations.

Wordlists for web content discovery

Web content discovery is the process of finding and identifying web pages, files, directories, or other resources that are hosted on a web server or application. Wordlists are often used in web content discovery to perform brute force attacks, which try to enumerate and request possible URLs by using a list of common names or extensions. Web content discovery can be done for legitimate reasons, such as testing the security or functionality of a web site or application, or for malicious reasons, such as finding vulnerabilities or sensitive information.

To perform a successful brute force attack, the wordlist should contain names or extensions that are likely to be used by the target web server or application. Some factors that can affect the choice of wordlist are:

The type of web server or application: Different web servers or applications may have different naming conventions or structures for their web resources, such as directories, files, parameters, etc. For example, some web servers may use default names for their directories, such as "admin", "images", or "css". Some web applications may use specific extensions for their files, su


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