KnowsMore - A Swiss Army Knife Tool For Pentesting Microsoft Active Directory (NTLM Hashes, BloodHound, NTDS And DCSync)

KnowsMore officially supports Python 3.8+.

Main features

• Import NTLM Hashes from .ntds output txt file (generated by CrackMapExec or secretsdump.py)
• Import NTLM Hashes from NTDS.dit and SYSTEM
• Import Cracked NTLM hashes from hashcat output file
• Import BloodHound ZIP or JSON file
• BloodHound importer (import JSON to Neo4J without BloodHound UI)
• Analyse the quality of password (length , lower case, upper case, digit, special and latin)
• Analyse similarity of password with company and user name
• Search for users, passwords and hashes
• Export all cracked credentials direct to BloodHound Neo4j Database as 'owned object'
• Other amazing features...

Getting stats

knowsmore --stats

This command will produce several statistics about the passwords like the output bellow

KnowsMore v0.1.4 by Helvio Junior
Active Directory, BloodHound, NTDS hashes and Password Cracks correlation tool
https://github.com/helviojunior/knowsmore

 [+] Startup parameters
     command line: knowsmore --stats 
     module: stats
     database file: knowsmore.db

 [+] start time 2023-01-11 03:59:20
[?] General Statistics
+-------+----------------+-------+
|   top | description    |   qty |
|-------+----------------+-------|
|     1 | Total Users    | 95369 |
|     2 | Unique Hashes  | 74299 |
|     3 | Cracked Hashes | 23177 |
|     4 | Cracked Users  | 35078 |
+-------+----------------+-------+

 [?] General Top 10 passwords
+-------+-------------+-------+
|   top | password    |   qty |
|-------+-------------+-------|
|     1 | password    |  1111 |
|     2 | 123456      |   824 |
|     3 | 123456789   |   815 |
|     4 | guest       |      553 |
|     5 | qwerty      |   329 |
|     6 | 12345678    |   277 |
|     7 | 111111      |   268 |
|     8 | 12345       |   202 |
|     9 | secret      |   170 |
|    10 | sec4us      |   165 |
+-------+-------------+-------+

 [?] Top 10 weak passwords by company name similarity
+-------+--------------+---------+----------------------+-------+
|   top | password     |   score |   company_similarity |   qty |
|-------+--------------+---------+----------------------+-------|
|     1 | company123   |    7024 |                   80 |  1111 |
|     2 | Company123   |    5209 |                   80 |   824 |
|     3 | company      |    3674 |                  100 |   553 |
|     4 | Company@10   |    2080 |                   80 |   329 |
|     5 | company10    |    1722 |                   86 |   268 |
|     6 | Company@2022 |    1242 |                   71 |   202 |
|     7 | Company@2024 |    1015 |                      71 |   165 |
|     8 | Company2022  |     978 |                   75 |   157 |
|     9 | Company10    |     745 |                   86 |   116 |
|    10 | Company21    |     707 |                   86 |   110 |
+-------+--------------+---------+----------------------+-------+

Installation

Simple

pip3 install --upgrade knowsmore

Note: If you face problem with dependency version Check the Virtual ENV file

Execution Flow

There is no an obligation order to import data, but to get better correlation data we suggest the following execution flow:

1. Create database file
2. Import BloodHound files
   1. Domains
   2. GPOs
   3. OUs
   4. Groups
   5. Computers
   6. Users
3. Import NTDS file
4. Import cracked hashes

Create database file

All data are stored in a SQLite Database

knowsmore --create-db

Importing BloodHound files

We can import all full BloodHound files into KnowsMore, correlate data, and sync it to Neo4J BloodHound Database. So you can use only KnowsMore to import JSON files directly into Neo4j database instead of use extremely slow BloodHound User Interface

# Bloodhound ZIP File
knowsmore --bloodhound --import-data ~/Desktop/client.zip

# Bloodhound JSON File
knowsmore --bloodhound --import-data ~/Desktop/20220912105336_users.json

Note: The KnowsMore is capable to import BloodHound ZIP File and JSON files, but we recommend to use ZIP file, because the KnowsMore will automatically order the files to better data correlation.

Sync data to Neo4j BloodHound database

# Bloodhound ZIP File
knowsmore --bloodhound --sync 10.10.10.10:7687 -d neo4j -u neo4j -p 12345678

Note: The KnowsMore implementation of bloodhount-importer was inpired from Fox-It BloodHound Import implementation. We implemented several changes to save all data in KnowsMore SQLite database and after that do an incremental sync to Neo4J database. With this strategy we have several benefits such as at least 10x faster them original BloodHound User interface.

Importing NTDS file

Option 1

Note: Import hashes and clear-text passwords directly from NTDS.dit and SYSTEM registry

knowsmore --secrets-dump -target LOCAL -ntds ~/Desktop/ntds.dit -system ~/Desktop/SYSTEM

Option 2

Note: First use the secretsdump to extract ntds hashes with the command bellow

secretsdump.py -ntds ntds.dit -system system.reg -hashes lmhash:ntlmhash LOCAL -outputfile ~/Desktop/client_name

After that import

knowsmore --ntlm-hash --import-ntds ~/Desktop/client_name.ntds

Generating a custom wordlist

knowsmore --word-list -o "~/Desktop/Wordlist/my_custom_wordlist.txt" --batch --name company_name

Importing cracked hashes

Cracking hashes

First extract all hashes to a txt file

# Extract NTLM hashes to file
nowsmore --ntlm-hash --export-hashes "~/Desktop/ntlm_hash.txt"

# Or, extract NTLM hashes from NTDS file
cat ~/Desktop/client_name.ntds | cut -d ':' -f4 > ntlm_hashes.txt

In order to crack the hashes, I usually use hashcat with the command bellow

# Wordlist attack
hashcat -m 1000 -a 0 -O -o "~/Desktop/cracked.txt" --remove "~/Desktop/ntlm_hash.txt" "~/Desktop/Wordlist/*"

# Mask attack
hashcat -m 1000 -a 3 -O --increment --increment-min 4 -o "~/Desktop/cracked.txt" --remove "~/Desktop/ntlm_hash.txt" ?a?a?a?a?a?a?a?a

importing hashcat output file

knowsmore --ntlm-hash --company clientCompanyName --import-cracked ~/Desktop/cracked.txt

Note: Change clientCompanyName to name of your company

Wipe sensitive data

As the passwords and his hashes are extremely sensitive data, there is a module to replace the clear text passwords and respective hashes.

Note: This command will keep all generated statistics and imported user data.

knowsmore --wipe

BloodHound Mark as owned

One User

During the assessment you can find (in a several ways) users password, so you can add this to the Knowsmore database

knowsmore --user-pass --username administrator --password Sec4US@2023

# or adding the company name

knowsmore --user-pass --username administrator --password Sec4US@2023 --company sec4us

Integrate all credentials cracked to Neo4j Bloodhound database

knowsmore --bloodhound --mark-owned 10.10.10.10 -d neo4j -u neo4j -p 123456

To remote connection make sure that Neo4j database server is accepting remote connection. Change the line bellow at the config file /etc/neo4j/neo4j.conf and restart the service.

server.bolt.listen_address=0.0.0.0:7687

Kubestroyer - Kubernetes Exploitation Tool

Kubestroyer

Kubestroyer aims to exploit Kubernetes clusters misconfigurations and be the swiss army knife of your Kubernetes pentests

About The Project

Kubestroyer is a Golang exploitation tool that aims to take advantage of Kubernetes clusters misconfigurations.

The tool is scanning known Kubernetes ports that can be exposed as well as exploiting them.

Getting Started

To get a local copy up and running, follow these simple example steps.

Prerequisites

• Go 1.19

  wget https://go.dev/dl/go1.19.4.linux-amd64.tar.gz
  tar -C /usr/local -xzf go1.19.4.linux-amd64.tar.gz

Installation

Use prebuilt binary

or

Using go install command :

$ go install github.com/Rolix44/Kubestroyer@latest

or

build from source:

1. Clone the repo

   $ git clone https://github.com/Rolix44/Kubestroyer.git

2. build the binary

   $ go build -o Kubestroyer cmd/kubestroyer/main.go 

Usage

Currently supported features

• Target

  • List of multiple targets
  • Input file as target

• Scanning

  • Known ports scan
  • Node port scan (30000 to 32767)
  • Port description

• Vulnerabilities

  • Annon RCE on Kubelet
    • Choose command to execute

Roadmap

• Choose the pod for anon RCE
• Etcd exploit
• Kubelet read-only API parsing for information disclosure

See the open issues for a full list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE.txt for more information.

Contact

Rolix - @Rolix_cy - rolixcy@protonmail.com

Project Link: https://github.com/Rolix44/Kubestroyer