Linkedin intelligence to scrap employee profiles for further password guessing or phishing attacks.
UPDATE: My buddy @h0useh3ad updated this to Python3. Thanks! https://github.com/h0useh3ad/LinkedinMama3
Original Scraper - @DisK0nn3ct (https://github.com/DisK0nn3cT/linkedin-gatherer)
Updated Scraper - @vysecurity (https://github.com/vysecurity/LinkedInt/blob/master/LinkedInt.py)
Modified by @bigb0ss
1) Simplified and cleaned up some script outputs.
2) Added random delays (0-3) between scraping pages.
3) Modified only output file to .csv.
4) Auto companyID search seemed to be unrealiable for some companies that have multiple IDs --> Modified to provide exact companyID from Linkedin. Go to the target company's "See all employees" page and use the "facetCurrentCompany=" values at a time.
git clone https://github.com/bigb0sss/LinkedinMama.git
pip install -r requirements.txt
$ python LinkedinMama.py -h
usage: LinkedinMama.py [-h] [-k KEYWORDS] [-c COMPANYID] [-e EMAIL]
[-n NAMING]
[*] Linkedin User Scraper
optional arguments:
-h, --help show this help message and exit
-k KEYWORDS, --keywords KEYWORDS
Keywords to search & create output to file
-c COMPANYID, --companyid COMPANYID
facetCurrentCompany= parameter value
-e EMAIL, --email EMAIL
Company email domain
-n NAMING, --naming NAMING
User naming scheme: [0] Auto (hunter.io) [1] FirstLast
[2] FirstMiddleLast [3] FLast [4] FirstL [5]
First.Last [6] Last.First
Run the script
python LinkedinMama.py
_ _____ _ _ _ ________ _____ _____ _ _ __ __ __ __
| | |_ _| \ | | |/ / ____| __ \_ _| \ | | \/ | /\ | \/ | /\
| | | | | \| | ' /| |__ | | | || | | \| | \ / | / \ | \ / | / \
| | | | | . ` | < | __| | | | || | | . ` | |\/| | / /\ \ | |\/| | / /\ \
| |____ _| |_| |\ | . \| |____| |__| || |_| |\ | | | |/ ____ \| | | |/ ____ \
|______|_____|_| \_|_|\_\______|_____/_____|_| \_|_| |_/_/ \_\_| |_/_/ \_\
[bigb0ss]
[] Linkedin Intelligence [] Original Scraper by @DisK0nn3ct [] Updated Scraper by @vysecurity [] This Scraper by @bigb0ss
[] Enter Linkedin Search Keyword (eg. "google"): tesla <-- Providing the keyword to search
[+] Creating Output File: tesla.csv
[] Provide "facetCurrentCompany=" ID: 1234 <-- Providing the companyID
[+] Using CompanyID: 1234
[] Enter Email Domain (eg. gmail.com): tesla.com <-- Providing the email domain
[] Naming Scheme for the Company
[0] Auto (hunter.io)
[1] FirstLast
[2] FirstMiddleLast
[3] FLast
[4] FirstL
[5] First.Last
[6] Last.First
[!] Select: 0. <-- Selecting the naming scheme used for the email addresses
[] Hunter.io is doing the job for you
[+] {first}.{last}
[+] Found first.last Naming Scheme
[+] Welcome to Linkedin!
[+] Login Success as
OR<br/>
python LinkedinMama.py -k tesla -c 1234 -e tesla.com -n 0
| | | | \ | | |/ / __| _ | \ | | \/ | /\ | \/ | /\
| | | | | | | ' /| | | | | || | | | | \ / | / \ | \ / | / \
| | | | | . ` | < | | | | | || | | . ` | |\/| | / /\ \ | |\/| | / /\ \
| |____ | |_| |\ | . | |__| || || |_| |\ | | | |/ | | | |/ \
|__||| _||______|_/____|| _|| |// __| |// \\
[bigb0ss]
[] Linkedin Intelligence [] Original Scraper by @DisK0nn3ct [] Updated Scraper by @vysecurity [] This Scraper by @bigb0ss
[+] Creating Output File: tesla.csv
[+] Using CompanyID: 1234
[] Hunter.io is doing the job for you
[+] {first}.{last}
[+] Found first.last Naming Scheme
[+] Welcome to Linkedin!
[+] Login Success as