SecLists

repository·master·Indexed 13 days ago

https://github.com/danielmiessler/seclists

A comprehensive collection of security testing resources, including usernames, passwords, URLs, fuzzing payloads, and web shells. It provides specialized wordlists for LLM safety testing (jailbreak prompts, bias, and privacy), DNS subdomain discovery, web application API fuzzing, and targeting specific environments like Spring Boot, Adobe Experience Manager, and Oracle E-Business Suite v11.

Tokens
19.1K
Snippets
73
Records
160
Agent score
98%

What's inside SecLists

  1. Overview of SecLists content

    master

    SecLists is a collection of multiple types of lists used during security assessments. It is designed to be pulled onto a testing machine to provide immediate access to various payloads and patterns.

    Common list types include:

    • Usernames and passwords
    • URLs and directory names
    • Sensitive data patterns
    • Fuzzing payloads
    • Web shells
  2. Overview of Moby Project wordlists in SecLists

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    The Moby Project provides public-domain lexical resources created by Grady Ward. Within the SecLists repository, this directory contains specific subsets of the Moby Project wordlists that are useful for security testing and lexical analysis.

    The available sub-directories in this repository include:

    • Moby Language II: Wordlists covering 5 different languages.
    • Moby Thesaurus II: A large collection containing 30,260 root words and over 2 million synonyms.
    • Moby Words II: A collection of various other useful wordlists.
  3. Overview of the Passwords directory

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    The Passwords directory contains various password lists used for credential guessing against targeted services. This includes popular lists like the RockYou lists (maintained by Rob Bowes) and specialized wordlists.

    Key Directory Conventions:

    • withcount folder: Contains password lists where each entry is accompanied by its occurrence count.
    • Compression: Lists larger than 100MB are stored in compressed formats to manage repository size.
  4. Use XSS wordlists for manual or automated testing

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    The XSS directory contains wordlists designed for testing Cross-Site Scripting (XSS) vulnerabilities. The wordlists are categorized into two types based on the testing method:

    1. human-friendly: Optimized for manual testing. These files contain comments and annotations that are helpful for humans but may interfere with automated tools.
    2. robot-friendly: Optimized for automated security tools like OWASP ZAP or Burp Suite. These are stripped of unnecessary metadata to ensure compatibility with scanners.

    When using robot-friendly lists, check the specific README.md file within the target subdirectory, as minor adjustments may be required for certain tools.

  5. Use EFF Dice wordlists for password generation

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    The EFF Dice wordlists are designed to facilitate the generation of secure, memorable passwords based on dice rolls. The lists are categorized by word length and complexity to match different dice-rolling scenarios (e.g., using 4 dice vs. more).

    To use these lists, select the file that matches your intended dice-rolling method and password complexity requirements. If you are building a tool that requires only the raw words without the dice-side metadata, use the files suffixed with _words.txt.

    ### Included files
    
    - `large.txt` - EFF's Large Word List
    - `large_words.txt` - EFF's Large Word List, with only the words present (i.e. no side numbers)
    - `small_1.txt` - EFF's Small Word List 1, for use with 4 dice and featuring only short words
    - `small_1_words.txt` - EFF's Small Word List 1, with only the words present (i.e. no side numbers)
    - `small_2.txt` - EFF's Small Word List 2, for use with 4 dice and featuring longer words that may be more memorable
    - `small_2_words.txt` - EFF's Small Word List 2, with only the words present (i.e. no side numbers)
  6. Prompt Categories for Testing Language Models (LLMs)

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    SecLists provides categorized prompt wordlists designed to evaluate the robustness, safety, and ethical boundaries of Large Language Models (LLMs). These categories help developers and security researchers test for:

    • Ethical and Safety Boundaries: Assessing adherence to guidelines and resistance to jailbreaks.
    • Bias Testing: Identifying biases related to gender, race, ethnicity, and geography.
    • Privacy and Data Leakage: Probing for training data memorization and the generation of PII (Personally Identifiable Information).
  7. Use wordlists for discovery and cracking

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    The Miscellaneous/Words directory provides wordlists in multiple languages. These lists are designed for use in security testing scenarios, specifically for:

    • File discovery: Finding hidden or non-obvious files on a system.
    • Directory discovery: Enumerating web directories and paths.
    • Password cracking: Providing common terms and language-specific words for dictionary attacks.