Pagodo - Automate Google Hacking Database Scraping And Searching

The goal of this project was to develop a passive Google dork script to collect potentially vulnerable web pages and applications on the Internet. There are 2 parts. The first is ghdb_scraper.py that retrieves Google Dorks and the second portion is pagodo.py that leverages the information gathered by ghdb_scraper.py.

What are Google Dorks?
The awesome folks at Offensive Security maintain the Google Hacking Database (GHDB) found here: https://www.exploit-db.com/google-hacking-database. It is a collection of Google searches, called dorks, that can be used to find potentially vulnerable boxes or other juicy info that is picked up by Google's search bots.

Installation
Scripts are written for Python 3.6+. Clone the git repository and install the requirements.

Google is blocking me!
If you start getting HTTP 503 errors, Google has rightfully detected you as a bot and will block your IP for a set period of time. The solution is to use proxychains and a bank of proxies to round robin the lookups.
Install proxychains4
Edit the /etc/proxychains4.conf configuration file to round robin the look ups through different proxy servers. In the example below, 2 different dynamic socks proxies have been set up with different local listening ports (9050 and 9051). Don't know how to utilize SSH and dynamic socks proxies? Do yourself a favor and pick up a copy of The Cyber Plumber's Handbook to learn all about Secure Shell (SSH) tunneling, port redirection, and bending traffic like a boss.
Throw proxychains4 in front of the Python script and each lookup will go through a different proxy (and thus source from a different IP). You could even tune down the -e delay time because you will be leveraging different proxy boxes.

ghdb_scraper.py
To start off, pagodo.py needs a list of all the current Google dorks. A datetimestamped file with the Google dorks and the indididual dork category dorks are also provided in the repo. Fortunately, the entire database can be pulled back with 1 GET request using ghdb_scraper.py. You can dump all dorks to a file, the individual dork categories to separate dork files, or the entire json blob if you want more contextual data about the dork.
To retrieve all dorks
To retrieve all dorks and write them to individual categories:
Dork categories:

categories = {      1: "Footholds",      2: "File Containing Usernames",      3: "Sensitives Directories",      4: "Web Server Detection",      5: "Vulnerable Files",      6: "Vulnerable Servers",      7: "Error Messages",      8: "File Containing Juicy Info",      9: "File Containing Passwords",      10: "Sensitive Online Shopping Info",      11: "Network or Vulnerability Data",      12: "Pages Containing Login Portals",      13: "Various Online devices",      14: "Advisories and Vulnerabilities",  }  

pagodo.py
Now that a file with the most recent Google dorks exists, it can be fed into pagodo.py using the -g switch to start collecting potentially vulnerable public applications. pagodo.py leverages the googlepython library to search Google for sites with the Google dork, such as:

intitle:"ListMail Login" admin -demo  

The -d switch can be used to specify a domain and functions as the Google search operator:

site:example.com  

Performing ~4600 search requests to Google as fast as possible will simply not work. Google will rightfully detect it as a bot and block your IP for a set period of time. In order to make the search queries appear more human, a couple of enhancements have been made. A pull request was made and accepted by the maintainer of the Python google module to allow for User-Agent randomization in the Google search queries. This feature is available in 1.9.3 and allows you to randomize the different user agents used for each search. This emulates the different browsers used in a large corporate environment.
The second enhancement focuses on randomizing the time between search queries. A minimum delay is specified using the -e option and a jitter factor is used to add time on to the minimum delay number. A list of 50 jitter times is created and one is randomly appended to the minimum delay time for each Google dork search.
Latter in the script, a random time is selected from the jitter array and added to the delay.
Experiment with the values, but the defaults successfully worked without Google blocking my IP. Note that it could take a few days (3 on average) to run so be sure you have the time.
To run it:

Conclusion
Comments, suggestions, and improvements are always welcome. Be sure to follow @opsdisk on Twitter for the latest updates.