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BloodHound is a monolithic web application composed of an embedded React frontend with Sigma.js and a Go based REST API backend. It is deployed with a Postgresql application database and a Neo4j graph database, and is fed by the SharpHound and AzureHound data collectors.
BloodHound uses graph theory to reveal the hidden and often unintended relationships within an Active Directory or Azure environment. Attackers can use BloodHound to easily identify highly complex attack paths that would otherwise be impossible to identify quickly. Defenders can use BloodHound to identify and eliminate those same attack paths. Both blue and red teams can use BloodHound to easily gain a deeper understanding of privilege relationships in an Active Directory or Azure environment.
BloodHound CE is created and maintained by the BloodHound Enterprise Team. The original BloodHound was created by @_wald0, @CptJesus, and @harmj0y.
The easiest way to get up and running is to use our pre-configured Docker Compose setup. The following steps will get BloodHound CE up and running with the least amount of effort.
FreshRSS curl -L https://ghst.ly/getbhce | docker compose -f - up
http://localhost:8080/ui/login. Login with a username of admin and the randomly generated password from the logsNOTE: going forward, the default docker-compose.yml example binds only to localhost (127.0.0.1). If you want to access BloodHound outside of localhost, you'll need to follow the instructions in examples/docker-compose/README.md to configure the host binding for the container.
# Verify if Docker Engine is Running
docker info
# Attempt to stop Neo4j Service if running (on Windows)
Stop-Service "Neo4j" -ErrorAction SilentlyContinue
https://github.com/SpecterOps/BloodHound/assets/12970156/ea9dc042-1866-4ccb-9839-933140cc38b9
Please check out the Contact page in our wiki for details on how to reach out with questions and suggestions.
A Python3 terminal application that contains 260+ Neo4j cyphers for BloodHound data sets.
BloodHound is a staple tool for every red teamer. However, there are some negative side effects based on its design. I will cover the biggest pain points I've experienced and what this tool aims to address:
JSON graphs, I need graph results in a line-by-line format .txt fileThis tool can also help blue teams to reveal detailed information about their Active Directory environments as well.
Take back control of your BloodHound data with cypherhound!
grep/cut/awk-friendly formatMake sure to have python3 installed and run:
python3 -m pip install -r requirements.txt
Start the program with: python3 cypherhound.py -u <neo4j_username> -p <neo4j_password>
The full command menu is shown below:
Command Menu
set - used to set search parameters for cyphers, double/single quotes not required for any sub-commands
sub-commands
user - the user to use in user-specific cyphers (MUST include @domain.name)
group - the group to use in group-specific cyphers (MUST include @domain.name)
computer - the computer to use in computer-specific cyphers (SHOULD include .domain.name or @domain.name)
regex - the regex to use in regex-specific cyphers
example
set user svc-test@domain.local
set group domain admins@domain.local
set computer dc01.domain.local
set regex .*((?i)web).*
run - used to run cyphers
parameters
cypher number - the number of the cypher to run
example
run 7
export - used to export cypher results to txt files
parameters
cypher number - the number of the cypher to run and then export
output filename - the number of the output file, extension not needed
raw - write raw output or just end object (optional)
example
export 31 results
export 42 results2 raw
list - used to show a list of cyphers
parameters
list type - the type of cyphers to list (general, user, group, computer, regex, all)
example
list general
list user
list group
list computer
list regex
list all
q, quit, exit - used to exit the program
clear - used to clear the terminal
help, ? - used to display this help menu
Neo4j database and URI
BloodHound 4.2.0, certain edges will not work for previous versionsWindows users must run pip3 install pyreadline3
raw or not) due to their unpredictable number of nodesAzure edgesPlease be descriptive with any issues you decide to open and if possible provide output (if applicable).
autobloody is a tool to automatically exploit Active Directory privilege escalation paths shown by BloodHound.
This tool automates the AD privesc between two AD objects, the source (the one we own) and the target (the one we want) if a privesc path exists in BloodHound database. The automation is composed of two steps:
bloodyAD packageBecause autobloody relies on bloodyAD, it supports authentication using cleartext passwords, pass-the-hash, pass-the-ticket or certificates and binds to LDAP services of a domain controller to perform AD privesc.
First if you run it on Linux, you must have libkrb5-dev installed on your OS in order for kerberos to work:
# Debian/Ubuntu/Kali
apt-get install libkrb5-dev
# Centos/RHEL
yum install krb5-devel
# Fedora
dnf install krb5-devel
# Arch Linux
pacman -S krb5A python package is available:
pip install autobloodyOr you can clone the repo:
git clone --depth 1 https://github.com/CravateRouge/autobloody
pip install .First data must be imported into BloodHound (e.g using SharpHound or BloodHound.py) and Neo4j must be running.
⚠️-ds and -dt values are case sensitive
Simple usage:
autobloody -u john.doe -p 'Password123!' --host 192.168.10.2 -dp 'neo4jP@ss' -ds 'JOHN.DOE@BLOODY.LOCAL' -dt 'BLOODY.LOCAL'Full help:
[bloodyAD]$ ./autobloody.py -h
usage: autobloody.py [-h] [--dburi DBURI] [-du DBUSER] -dp DBPASSWORD -ds DBSOURCE -dt DBTARGET [-d DOMAIN] [-u USERNAME] [-p PASSWORD] [-k] [-c CERTIFICATE] [-s] --host HOST
AD Privesc Automation
options:
-h, --help show this help message and exit
--dburi DBURI The host neo4j is running on (default is "bolt://localhost:7687")
-du DBUSER, --dbuser DBUSER
Neo4j username to use (default is "neo4j")
-dp DBPASSWORD, --dbpassword DBPASSWORD
Neo4j password to use
-ds DBSOURCE, --dbsource DBSOURCE
Case sensitive label of the source node (name property in bloodhound)
-dt DBTARGET, --dbtarget DBTARGET
Case sensitive label of the target node (name property in bloodhound)
-d DOMAIN, --domain DOMAIN
Domain used for NTLM authentication
-u USERNAME, --username USERNAME
Username used for NTLM authentication
-p PASSWORD, --password PASSWORD
Cleartext password or LMHASH:NTHASH for NTLM authentication
-k, --kerberos
-c CERTIFICATE, --certificate CERTIFICATE
Certificate authentication, e.g: "path/to/key:path/to/cert"
-s, --secure Try to use LDAP over TLS aka LDAPS (default is LDAP)
--host HOST Hostname or IP of the DC (ex: my.dc.local or 172.16.1.3)First a privesc path is found using the Dijkstra's algorithm implemented into the Neo4j's GDS library. The Dijkstra's algorithm allows to solve the shortest path problem on a weighted graph. By default the edges created by BloodHound don't have weight but a type (e.g MemberOf, WriteOwner). A weight is then added to each edge accordingly to the type of edge and the type of node reached (e.g user,group,domain).
Once a path is generated, autobloody will connect to the DC and execute the path and clean what is reversible (everything except ForcePasswordChange and setOwner).
For now, only the following BloodHound edges are currently supported for automatic exploitation:
nuvola (with the lowercase n) is a tool to dump and perform automatic and manual security analysis on AWS environments configurations and services using predefined, extensible and custom rules created using a simple Yaml syntax.
The general idea behind this project is to create an abstracted digital twin of a cloud platform. For a more concrete example: nuvola reflects the BloodHound traits used for Active Directory analysis but on cloud environments (at the moment only AWS).
The usage of a graph database also increases the possibility of finding different and innovative attack paths and can be used as an offline, centralised and lightweight digital twin.
docker-compose installedawscli with full access to the cloud resources, better if in ReadOnly mode (the policy arn:aws:iam::aws:policy/ReadOnlyAccess is fine)git clone --depth=1 https://github.com/primait/nuvola.git; cd nuvola.env file to set your DB username/password/URLcp .env_example .env;make startmake build./nuvola dump -profile default_RO -outputdir ~/DumpDumpFolder -format zip./nuvola assess -import ~/DumpDumpFolder/nuvola-default_RO_20220901.zip./nuvola assessTo get started with nuvola and its database schema, check out the nuvola Wiki.
No data is sent or shared with Prima Assicurazioni.
nuvola uses graph theory to reveal possible attack paths and security misconfigurations on cloud environments.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this repository and program. If not, see http://www.gnu.org/licenses/.
