Uscrapper - Powerful OSINT Webscraper For Personal Data Collection

Introducing Uscrapper 2.0, A powerfull OSINT webscrapper that allows users to extract various personal information from a website. It leverages web scraping techniques and regular expressions to extract email addresses, social media links, author names, geolocations, phone numbers, and usernames from both hyperlinked and non-hyperlinked sources on the webpage, supports multithreading to make this process faster, Uscrapper 2.0 is equipped with advanced Anti-webscrapping bypassing modules and supports webcrawling to scrape from various sublinks within the same domain. The tool also provides an option to generate a report containing the extracted details.

Extracted Details:

Uscrapper extracts the following details from the provided website:

• Email Addresses: Displays email addresses found on the website.
• Social Media Links: Displays links to various social media platforms found on the website.
• Author Names: Displays the names of authors associated with the website.
• Geolocations: Displays geolocation information associated with the website.
• Non-Hyperlinked Details: Displays non-hyperlinked details found on the website including email addresses phone numbers and usernames.

Whats New?:

Uscrapper 2.0:

• Introduced multiple modules to bypass anti-webscrapping techniques.
• Introducing Crawl and scrape: an advanced crawl and scrape module to scrape the websites from within.
• Implemented Multithreading to make these processes faster.

Installation Steps:

git clone https://github.com/z0m31en7/Uscrapper.git

cd Uscrapper/install/ 
chmod +x ./install.sh && ./install.sh      #For Unix/Linux systems

Usage:

To run Uscrapper, use the following command-line syntax:

python Uscrapper-v2.0.py [-h] [-u URL] [-c (INT)] [-t THREADS] [-O] [-ns]

Arguments:

• -h, --help: Show the help message and exit.
• -u URL, --url URL: Specify the URL of the website to extract details from.
• -c INT, --crawl INT: Specify the number of links to crawl
• -t INT, --threads INT: Specify the number of threads to use while crawling and scraping.
• -O, --generate-report: Generate a report file containing the extracted details.
• -ns, --nonstrict: Display non-strict usernames during extraction.

Note:

• Uscrapper relies on web scraping techniques to extract information from websites. Make sure to use it responsibly and in compliance with the website's terms of service and applicable laws.

• The accuracy and completeness of the extracted details depend on the structure and content of the website being analyzed.

• To bypass some Anti-Webscrapping methods we have used selenium which can make the overall process slower.

Contribution:

• Make a pull request with all the necessary details and it will be merged after a review.
• You can contribute by making the regular expressions more efficient and accurate, or by suggesting some more features that can be added.

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ScrapPY - A Python Utility For Scraping Manuals, Documents, And Other Sensitive PDFs To Generate Wordlists That Can Be Utilized By Offensive Security Tools

ScrapPY is a Python utility for scraping manuals, documents, and other sensitive PDFs to generate targeted wordlists that can be utilized by offensive security tools to perform brute force, forced browsing, and dictionary attacks. ScrapPY performs word frequency, entropy, and metadata analysis, and can run in full output modes to craft custom wordlists for targeted attacks. The tool dives deep to discover keywords and phrases leading to potential passwords or hidden directories, outputting to a text file that is readable by tools such as Hydra, Dirb, and Nmap. Expedite initial access, vulnerability discovery, and lateral movement with ScrapPY!

Install:

Download Repository:

$ mkdir ScrapPY
$ cd ScrapPY/
$ sudo git clone https://github.com/RoseSecurity/ScrapPY.git

Install Dependencies:

$ pip3 install -r requirements.txt

ScrapPY Usage:

usage: ScrapPY.py [-h] [-f FILE] [-m {word-frequency,full,metadata,entropy}] [-o OUTPUT]

Output metadata of document:

$ python3 ScrapPY.py -f example.pdf -m metadata

Output top 100 frequently used keywords to a file name Top_100_Keywords.txt:

$ python3 ScrapPY.py -f example.pdf -m word-frequency -o Top_100_Keywords.txt

Output all keywords to default ScrapPY.txt file:

$ python3 ScrapPY.py -f example.pdf

Output top 100 keywords with highest entropy rating:

$ python3 ScrapPY.py -f example.pdf -m entropy

ScrapPY Output:

# ScrapPY outputs the ScrapPY.txt file or specified name file to the directory in which the tool was ran. To view the first fifty lines of the file, run this command:

$ head -50 ScrapPY.txt

# To see how many words were generated, run this command:

$ wc -l ScrapPY.txt

Integration with Offensive Security Tools:

Easily integrate with tools such as Dirb to expedite the process of discovering hidden subdirectories:

root@RoseSecurity:~# dirb http://192.168.1.123/ /root/ScrapPY/ScrapPY.txt

-----------------
DIRB v2.21
By The Dark Raver
-----------------

START_TIME: Fri May 16 13:41:45 2014
URL_BASE: http://192.168.1.123/
WORDLIST_FILES: /root/ScrapPY/ScrapPY.txt

-----------------

GENERATED WORDS: 4592

---- Scanning URL: http://192.168.1.123/ ----
==> DIRECTORY: http://192.168.1.123/vi/
+ http://192.168.1.123/programming (CODE:200|SIZE:2726)
+ http://192.168.1.123/s7-logic/ (CODE:403|SIZE:1122)
==> DIRECTORY: http://192.168.1.123/config/
==> DIRECTORY: http://192.168.1.123/docs/
==> DIRECTORY: http://192.168.1.123/external/

Utilize ScrapPY with Hydra for advanced brute force attacks:

root@RoseSecurity:~# hydra -l root -P /root/ScrapPY/ScrapPY.txt -t 6 ssh://192.168.1.123
Hydra v7.6 (c)2013 by van Hauser/THC & David Maciejak - for legal purposes only

Hydra (http://www.thc.org/thc-hydra) starting at 2014-05-19 07:53:33
[DATA] 6 tasks, 1 server, 1003 login tries (l:1/p:1003), ~167 tries per task
[DATA] attacking service ssh on port 22

Enhance Nmap scripts with ScrapPY wordlists:

nmap -p445 --script smb-brute.nse --script-args userdb=users.txt,passdb=ScrapPY.txt 192.168.1.123

Future Development:

• Allow for custom output file naming and increased verbosity
• Integrate different modes of operation including word frequency analysis
• Allow for metadata analysis
• Search for high-entropy data
• Search for path-like data
• Implement image OCR to enumerate data from images in PDFs
• Allow for processing of multiple PDFs