Lil Pwny: auditing Active Directory passwords using Python

Lil Pwny

Lil Pwny is a Python application to perform an offline audit of NTLM hashes of users’ passwords, recovered from Active Directory, against known compromised passwords from Have I Been Pwned. The usernames of any accounts matching HIBP will be returned in a .txt file

There are also additional features:

  • Ability to provide a list of your passwords to check AD users against. This allows you to check user passwords against passwords relevant to your organization that you suspect people might be using. These are NTLM hashed, and AD hashes are then compared with this as well as the HIBP hashes.
  • Return a list of accounts using the same passwords. Useful for finding users using the same password for their administrative and standard accounts.

Installation

pip install lil-pwny

Use

Active Directory passwords

Example:

lil-pwny -hibp ~/hibp_hashes.txt -ad ~/ad_ntlm_hashes.txt -a ~/additional_passwords.txt -o ~/Desktop/Output -m -d

use of the -m flag will load the HIBP hashes into memory, which will allow for faster searching. Note this will require at least 24GB of available memory.

Getting input files

Step 1: Get an IFM AD database dump

On a domain controller use ntdsutil to generate an IFM dump of your AD domain. Run the following in an elevated PowerShell window:

ntdsutil
activate instance ntds
ifm
create full **output path**

Step 2: Recover NTLM hashes from this output

To recover the NTLM hashes from the AD IFM data, the Powershell module DSInternals is required.

Once installed, use the SYSTEM hive in the IFM data to recover the hashes in the format usernme:hash and save them to the file ad_ntlm_hashes.txt

$bootKey = Get-BootKey -SystemHivePath .\registry\SYSTEM

Get-ADDBAccount -All -DBPath .\Active Directory\ntds.dit -BootKey $bootKey | Format-Custom -View HashcatNT | Out-File ad_ntlm_hashes.txt -Encoding ASCII

Step 3: Download the latest HIBP hash file

The file can be downloaded from here

The latest version of the hash file contains around 551 million hashes.

Tutorial

Copyright (C) 2020

Source: https://github.com/PaperMtn/