BruteLoops

A dead simple library providing the foundational logic for efficient password brute force attacks against authentication interfaces.

Documentation

Documentation can be found here.

If you're looking for the old example modules...

See BFG.

The examples have been offloaded to a distinct project to minimize code and packaging issues. Database and attack capabilities have also been merged into a single binary.

Key Features

• Protocol agnostic - If a callback can be written in Python, BruteLoops can be used to attack it
• SQLite support - All usernames, passwords, and credentials are maintained in an SQLite database.
  • A companion utility (dbmanager.py) that creates and manages input databases accompanies BruteLoops
• Spray and Stuffing Attacks in One Tool - BruteLoops supports both spray and stuffing attacks in the same attack logic and database, meaning that you can configure a single database and run the attack without heavy reconfiguration and confusion.
• Guess scheduling - Each username in the SQLite database is configured with a timestamp that is updated after each authentication event. This means we can significantly reduce likelihood of locking accounts by scheduling each authentication event with precision.
• Fine-grained configurability to avoid lockout events - Microsoft's lockout policies can be matched 1-to-1 using BruteLoop's parameters:
  • auth_threshold = Lockout Threshold
  • max_auth_jitter = Lockout Observation Window
  • Timestampes associated with each authentication event are tracked in BruteLoops' SQLite database. Each username receives a distinct timestamp to assure that authentication events are highly controlled.
• Attack resumption - Stopping and resuming an attack is possible without worrying about losing your place in the attack or locking accounts.
• Multiprocessing - Speed up attacks using multiprocessing! By configuring the parallel guess count, you're effectively telling BruteLoops how many usernames to guess in parallel.
• Logging - Each authentication event can optionally logged to disk. This information can be useful during red teams by providing customers with a detailed attack timeline that can be mapped back to logged events.
• Breakers - Breakers behave like circuit breakers. An exception can be raised x number of times before ending the attack loop. They can reset after a given period of time as well, allowing for configurations like "Exit after 6 ConnectionErrors occur".

Dependencies

BruteLoops requires Python3.7 or newer and SQLAlchemy 1.3.0, the latter of which can be obtained via pip and the requirements.txt file in this repository: python3.7 -m pip install -r requirements.txt

Installation

git clone https://github.com/arch4ngel/bruteloops
cd bruteloops
python3 -m pip install -r requirements.txt