Pros of SecLists

• More comprehensive, covering a wider range of security testing scenarios
• Better organized into categories (e.g., passwords, usernames, fuzzing)
• Regularly updated with contributions from the security community

Cons of SecLists

• Larger file size, which may be overwhelming for simple testing needs
• Some lists may contain potentially offensive content
• Requires more time to navigate and find specific lists

Code Comparison

big-list-of-naughty-strings:

# Example usage
with open('blns.txt') as f:
    strings = f.read().splitlines()

SecLists:

# Example usage (using wfuzz)
wfuzz -c -z file,/path/to/SecLists/Passwords/Common-Credentials/10-million-password-list-top-1000000.txt http://example.com/login.php?username=admin&password=FUZZ

The big-list-of-naughty-strings is a single file containing various problematic strings, making it easy to use in simple testing scenarios. SecLists, on the other hand, provides multiple files organized into directories, offering more flexibility for different security testing needs but requiring more specific file selection.

Pros of List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words

• Focused specifically on profanity and offensive language
• Available in multiple languages
• Simpler structure, easier to integrate for basic profanity filtering

Cons of List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words

• Limited scope compared to big-list-of-naughty-strings
• Lacks context-specific strings and edge cases
• May not cover all potential input validation scenarios

Code Comparison

List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words:

2g1c
2 girls 1 cup
acrotomophilia
anal
anilingus
anus

big-list-of-naughty-strings:

undefined
undef
null
NULL
(null)
nil
NIL
true
false
True
False

The List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words repository focuses on explicit profanity and offensive terms, while big-list-of-naughty-strings covers a broader range of potentially problematic inputs, including programming-related strings, Unicode characters, and various edge cases for input validation.

big-list-of-naughty-strings is more comprehensive and suitable for thorough input validation and security testing, while List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words is better suited for simple profanity filtering in multiple languages.

Pros of corpora

• Broader scope: Contains diverse datasets beyond just problematic strings
• More structured: Data organized into categories and subcategories
• Regularly updated: Active community contributions and maintenance

Cons of corpora

• Less focused: Not specifically tailored for testing edge cases or security
• Larger size: May be overkill for simple string validation tasks
• More complex: Requires more effort to navigate and utilize specific datasets

Code comparison

big-list-of-naughty-strings:

with open('blns.txt', 'r') as f:
    naughty_strings = f.read().splitlines()

corpora:

import json
with open('corpora/data/technology/programming_languages.json') as f:
    programming_languages = json.load(f)['programming_languages']

The big-list-of-naughty-strings is a simple text file, while corpora uses JSON format for structured data. corpora requires parsing JSON and navigating the data structure, whereas big-list-of-naughty-strings can be used directly as a list of strings.

Both repositories serve different purposes: big-list-of-naughty-strings focuses on problematic input for testing, while corpora provides a wide range of curated datasets for various applications.

Pros of google-10000-english

• Contains a comprehensive list of common English words, useful for various language-related tasks
• Organized by frequency, allowing for easy selection of most common words
• Simple and straightforward format, easy to integrate into projects

Cons of google-10000-english

• Limited to English words, not suitable for testing edge cases or special characters
• Lacks variety in string types (e.g., no numbers, symbols, or Unicode characters)
• Not designed for security testing or input validation scenarios

Code Comparison

big-list-of-naughty-strings:

# Example strings
"undefined"
"undef"
"null"
"NULL"
"(null)"

google-10000-english:

# Example words
the
of
and
to
a

The big-list-of-naughty-strings repository contains a diverse set of problematic strings for testing input validation and edge cases. It includes various types of strings that could potentially cause issues in software systems.

In contrast, google-10000-english provides a list of common English words, sorted by frequency. This repository is more suitable for natural language processing tasks, vocabulary building, or general language-related applications.

While big-list-of-naughty-strings focuses on identifying potential vulnerabilities and edge cases, google-10000-english serves as a resource for common English vocabulary. The choice between these repositories depends on the specific requirements of your project, whether it's security testing or language processing.

Pros of english-words

• Comprehensive list of English words, useful for various language-related applications
• Regularly updated and maintained, ensuring accuracy and relevance
• Simple and straightforward structure, easy to integrate into projects

Cons of english-words

• Limited to English language only, not suitable for multilingual applications
• Lacks special characters, symbols, or edge cases found in big-list-of-naughty-strings
• May not be as effective for security testing or input validation purposes

Code Comparison

english-words:

aback
abacus
abandon
abandoned
abandoning
abandonment

big-list-of-naughty-strings:

undefined
undef
null
NULL
(null)
nil
NIL
true
false
True
False

The english-words repository contains a simple list of English words, one per line. In contrast, big-list-of-naughty-strings includes various types of strings that could potentially cause issues in software applications, such as null values, boolean expressions, and special characters.

While english-words is ideal for general language processing tasks, big-list-of-naughty-strings is more suited for testing input validation, security, and edge cases in software development.