A Python based ingestor for BloodHound
MIT License
BloodHound.py is a Python based ingestor for BloodHound, based on Impacket.
This version of BloodHound.py is only compatible with BloodHound 4.2 and 4.3. For BloodHound CE, check out the bloodhound-ce branch
BloodHound.py currently has the following limitations:
You can install the ingestor via pip with pip install bloodhound
, or by cloning this repository and running pip install .
from the project directory.
BloodHound.py requires impacket
, ldap3
and dnspython
to function.
The installation will add a command line tool bloodhound-python
to your PATH.
To use the ingestor, at a minimum you will need credentials of the domain you're logging in to.
You will need to specify the -u
option with a username of this domain (or username@domain
for a user in a trusted domain). If you have your DNS set up properly and the AD domain is in your DNS search list, then BloodHound.py will automatically detect the domain for you. If not, you have to specify it manually with the -d
option.
By default BloodHound.py will query LDAP and the individual computers of the domain to enumerate users, computers, groups, trusts, sessions and local admins.
If you want to restrict collection, specify the --collectionmethod
parameter, which supports the following options (similar to SharpHound):
Multiple collectionmethods should be separated by a comma, for example: -c Group,LocalAdmin
You can override some of the automatic detection options, such as the hostname of the primary Domain Controller if you want to use a different Domain Controller with -dc
, or specify your own Global Catalog with -gc
.
docker build -t bloodhound .
docker run -v ${PWD}:/bloodhound-data -it bloodhound
bloodhound-python
inside the container, all data will be stored in the path from where you start the container.BloodHound.py was originally written by Dirk-jan Mollema, Edwin van Vliet and Matthijs Gielen from Fox-IT (NCC Group). BloodHound.py is currently maintained by Dirk-jan Mollema from Outsider Security. The implementation and data model is based on the original tool from SpecterOps. Many thanks to everyone who contributed by testing, submitting issues and pull requests over the years.