Login
sttr is command line software that allows you to quickly run various transformation operations on the string.
FreshRSS // With input prompt
sttr
// Direct input
sttr md5 "Hello World"
// File input
sttr md5 file.text
sttr base64-encode image.jpg
// Reading from different processor like cat, curl, printf etc..
echo "Hello World" | sttr md5
cat file.txt | sttr md5
// Writing output to a file
sttr yaml-json file.yaml > file-output.json
You can run the below curl to install it somewhere in your PATH for easy use. Ideally it will be installed at ./bin folder
curl -sfL https://raw.githubusercontent.com/abhimanyu003/sttr/main/install.sh | sh
curl -sS https://webi.sh/sttr | sh
curl.exe https://webi.ms/sttr | powershell
See here
If you are on macOS and using Homebrew, you can install sttr with the following:
brew tap abhimanyu003/sttr
brew install sttr
sudo snap install sttr
yay -S sttr-bin
scoop bucket add sttr https://github.com/abhimanyu003/scoop-bucket.git
scoop install sttr
go install github.com/abhimanyu003/sttr@latest
Download the pre-compiled binaries from the Release! page and copy them to the desired location.
sttr command.// For interactive menu
sttr
// Provide your input
// Press two enter to open operation menu
// Press `/` to filter various operations.
// Can also press UP-Down arrows select various operations.
sttr -h
// Example
sttr zeropad -h
sttr md5 -h
sttr {command-name} {filename}
sttr base64-encode image.jpg
sttr md5 file.txt
sttr md-html Readme.md
sttr yaml-json file.yaml > file-output.json
curl https: //jsonplaceholder.typicode.com/users | sttr json-yaml
sttr md5 hello | sttr base64-encode
echo "Hello World" | sttr base64-encode | sttr md5
These are the few locations where sttr was highlighted, many thanks to all of you. Please feel free to add any blogs/videos you may have made that discuss sttr to the list.
To install headerpwn, run the following command:
go install github.com/devanshbatham/headerpwn@v0.0.3
headerpwn allows you to test various headers on a target URL and analyze the responses. Here's how to use the tool:
-url flag.-headers flag to specify the path to this file.Example usage:
headerpwn -url https://example.com -headers my_headers.txt
my_headers.txt should be like below:Proxy-Authenticate: foobar
Proxy-Authentication-Required: foobar
Proxy-Authorization: foobar
Proxy-Connection: foobar
Proxy-Host: foobar
Proxy-Http: foobar
Follow following steps to proxy requests through Burp Suite:
Export Burp's Certificate:
127.0.0.1:8080
Install Burp's Certificate:
You should be all set:
headerpwn -url https://example.com -headers my_headers.txt -proxy 127.0.0.1:8080
The headers.txt file is compiled from various sources, including the SecLists">Seclists project. These headers are used for testing purposes and provide a variety of scenarios for analyzing how servers respond to different headers.
An open-source, prototype implementation of property graphs for JavaScript based on the esprima parser, and the EsTree SpiderMonkey Spec. JAW can be used for analyzing the client-side of web applications and JavaScript-based programs.
This project is licensed under GNU AFFERO GENERAL PUBLIC LICENSE V3.0. See here for more information.
JAW has a Github pages website available at https://soheilkhodayari.github.io/JAW/.
Release Notes:
JAW-V2 branch.JAW-V1 branch.The architecture of the JAW is shown below.
JAW can be used in two distinct ways:
Arbitrary JavaScript Analysis: Utilize JAW for modeling and analyzing any JavaScript program by specifying the program's file system path.
Web Application Analysis: Analyze a web application by providing a single seed URL.
Use the collected web resources to create a Hybrid Program Graph (HPG), which will be imported into a Neo4j database.
Optionally, supply the HPG construction module with a mapping of semantic types to custom JavaScript language tokens, facilitating the categorization of JavaScript functions based on their purpose (e.g., HTTP request functions).
Query the constructed Neo4j graph database for various analyses. JAW offers utility traversals for data flow analysis, control flow analysis, reachability analysis, and pattern matching. These traversals can be used to develop custom security analyses.
JAW also includes built-in traversals for detecting client-side CSRF, DOM Clobbering and request hijacking vulnerabilities.
The outputs will be stored in the same folder as that of input.
The installation script relies on the following prerequisites: - Latest version of npm package manager (node js) - Any stable version of python 3.x - Python pip package manager
Afterwards, install the necessary dependencies via:
$ ./install.sh
For detailed installation instructions, please see here.
You can run an instance of the pipeline in a background screen via:
$ python3 -m run_pipeline --conf=config.yaml
The CLI provides the following options:
$ python3 -m run_pipeline -h
usage: run_pipeline.py [-h] [--conf FILE] [--site SITE] [--list LIST] [--from FROM] [--to TO]
This script runs the tool pipeline.
optional arguments:
-h, --help show this help message and exit
--conf FILE, -C FILE pipeline configuration file. (default: config.yaml)
--site SITE, -S SITE website to test; overrides config file (default: None)
--list LIST, -L LIST site list to test; overrides config file (default: None)
--from FROM, -F FROM the first entry to consider when a site list is provided; overrides config file (default: -1)
--to TO, -T TO the last entry to consider when a site list is provided; overrides config file (default: -1)
Input Config: JAW expects a .yaml config file as input. See config.yaml for an example.
Hint. The config file specifies different passes (e.g., crawling, static analysis, etc) which can be enabled or disabled for each vulnerability class. This allows running the tool building blocks individually, or in a different order (e.g., crawl all webapps first, then conduct security analysis).
For running a quick example demonstrating how to build a property graph and run Cypher queries over it, do:
$ python3 -m analyses.example.example_analysis --input=$(pwd)/data/test_program/test.js
This module collects the data (i.e., JavaScript code and state values of web pages) needed for testing. If you want to test a specific JavaScipt file that you already have on your file system, you can skip this step.
JAW has crawlers based on Selenium (JAW-v1), Puppeteer (JAW-v2, v3) and Playwright (JAW-v3). For most up-to-date features, it is recommended to use the Puppeteer- or Playwright-based versions.
This web crawler employs foxhound, an instrumented version of Firefox, to perform dynamic taint tracking as it navigates through webpages. To start the crawler, do:
$ cd crawler
$ node crawler-taint.js --seedurl=https://google.com --maxurls=100 --headless=true --foxhoundpath=<optional-foxhound-executable-path>
The foxhoundpath is by default set to the following directory: crawler/foxhound/firefox which contains a binary named firefox.
Note: you need a build of foxhound to use this version. An ubuntu build is included in the JAW-v3 release.
To start the crawler, do:
$ cd crawler
$ node crawler.js --seedurl=https://google.com --maxurls=100 --browser=chrome --headless=true
See here for more information.
To start the crawler, do:
$ cd crawler/hpg_crawler
$ vim docker-compose.yaml # set the websites you want to crawl here and save
$ docker-compose build
$ docker-compose up -d
Please refer to the documentation of the hpg_crawler here for more information.
To generate an HPG for a given (set of) JavaScript file(s), do:
$ node engine/cli.js --lang=js --graphid=graph1 --input=/in/file1.js --input=/in/file2.js --output=$(pwd)/data/out/ --mode=csv
optional arguments:
--lang: language of the input program
--graphid: an identifier for the generated HPG
--input: path of the input program(s)
--output: path of the output HPG, must be i
--mode: determines the output format (csv or graphML)
To import an HPG inside a neo4j graph database (docker instance), do:
$ python3 -m hpg_neo4j.hpg_import --rpath=<path-to-the-folder-of-the-csv-files> --id=<xyz> --nodes=<nodes.csv> --edges=<rels.csv>
$ python3 -m hpg_neo4j.hpg_import -h
usage: hpg_import.py [-h] [--rpath P] [--id I] [--nodes N] [--edges E]
This script imports a CSV of a property graph into a neo4j docker database.
optional arguments:
-h, --help show this help message and exit
--rpath P relative path to the folder containing the graph CSV files inside the `data` directory
--id I an identifier for the graph or docker container
--nodes N the name of the nodes csv file (default: nodes.csv)
--edges E the name of the relations csv file (default: rels.csv)
In order to create a hybrid property graph for the output of the hpg_crawler and import it inside a local neo4j instance, you can also do:
$ python3 -m engine.api <path> --js=<program.js> --import=<bool> --hybrid=<bool> --reqs=<requests.out> --evts=<events.out> --cookies=<cookies.pkl> --html=<html_snapshot.html>
Specification of Parameters:
<path>: absolute path to the folder containing the program files for analysis (must be under the engine/outputs folder).--js=<program.js>: name of the JavaScript program for analysis (default: js_program.js).--import=<bool>: whether the constructed property graph should be imported to an active neo4j database (default: true).--hybrid=bool: whether the hybrid mode is enabled (default: false). This implies that the tester wants to enrich the property graph by inputing files for any of the HTML snapshot, fired events, HTTP requests and cookies, as collected by the JAW crawler.--reqs=<requests.out>: for hybrid mode only, name of the file containing the sequence of obsevered network requests, pass the string false to exclude (default: request_logs_short.out).--evts=<events.out>: for hybrid mode only, name of the file containing the sequence of fired events, pass the string false to exclude (default: events.out).--cookies=<cookies.pkl>: for hybrid mode only, name of the file containing the cookies, pass the string false to exclude (default: cookies.pkl).--html=<html_snapshot.html>: for hybrid mode only, name of the file containing the DOM tree snapshot, pass the string false to exclude (default: html_rendered.html).For more information, you can use the help CLI provided with the graph construction API:
$ python3 -m engine.api -h
The constructed HPG can then be queried using Cypher or the NeoModel ORM.
You should place and run your queries in analyses/<ANALYSIS_NAME>.
You can use the NeoModel ORM to query the HPG. To write a query:
example_query_orm.py in the analyses/example folder.$ python3 -m analyses.example.example_query_orm
For more information, please see here.
You can use Cypher to write custom queries. For this:
example_query_cypher.py in the analyses/example folder.$ python3 -m analyses.example.example_query_cypher
For more information, please see here.
This section describes how to configure and use JAW for vulnerability detection, and how to interpret the output. JAW contains, among others, self-contained queries for detecting client-side CSRF and DOM Clobbering
Step 1. enable the analysis component for the vulnerability class in the input config.yaml file:
request_hijacking:
enabled: true
# [...]
#
domclobbering:
enabled: false
# [...]
cs_csrf:
enabled: false
# [...]
Step 2. Run an instance of the pipeline with:
$ python3 -m run_pipeline --conf=config.yaml
Hint. You can run multiple instances of the pipeline under different screens:
$ screen -dmS s1 bash -c 'python3 -m run_pipeline --conf=conf1.yaml; exec sh'
$ screen -dmS s2 bash -c 'python3 -m run_pipeline --conf=conf2.yaml; exec sh'
$ # [...]
To generate parallel configuration files automatically, you may use the generate_config.py script.
The outputs will be stored in a file called sink.flows.out in the same folder as that of the input. For Client-side CSRF, for example, for each HTTP request detected, JAW outputs an entry marking the set of semantic types (a.k.a, semantic tags or labels) associated with the elements constructing the request (i.e., the program slices). For example, an HTTP request marked with the semantic type ['WIN.LOC'] is forgeable through the window.location injection point. However, a request marked with ['NON-REACH'] is not forgeable.
An example output entry is shown below:
[*] Tags: ['WIN.LOC']
[*] NodeId: {'TopExpression': '86', 'CallExpression': '87', 'Argument': '94'}
[*] Location: 29
[*] Function: ajax
[*] Template: ajaxloc + "/bearer1234/"
[*] Top Expression: $.ajax({ xhrFields: { withCredentials: "true" }, url: ajaxloc + "/bearer1234/" })
1:['WIN.LOC'] variable=ajaxloc
0 (loc:6)- var ajaxloc = window.location.href
This entry shows that on line 29, there is a $.ajax call expression, and this call expression triggers an ajax request with the url template value of ajaxloc + "/bearer1234/, where the parameter ajaxloc is a program slice reading its value at line 6 from window.location.href, thus forgeable through ['WIN.LOC'].
In order to streamline the testing process for JAW and ensure that your setup is accurate, we provide a simple node.js web application which you can test JAW with.
First, install the dependencies via:
$ cd tests/test-webapp
$ npm install
Then, run the application in a new screen:
$ screen -dmS jawwebapp bash -c 'PORT=6789 npm run devstart; exec sh'
For more information, visit our wiki page here. Below is a table of contents for quick access.
Pull requests are always welcomed. This project is intended to be a safe, welcoming space, and contributors are expected to adhere to the contributor code of conduct.
If you use the JAW for academic research, we encourage you to cite the following paper:
@inproceedings{JAW,
title = {JAW: Studying Client-side CSRF with Hybrid Property Graphs and Declarative Traversals},
author= {Soheil Khodayari and Giancarlo Pellegrino},
booktitle = {30th {USENIX} Security Symposium ({USENIX} Security 21)},
year = {2021},
address = {Vancouver, B.C.},
publisher = {{USENIX} Association},
}
JAW has come a long way and we want to give our contributors a well-deserved shoutout here!
@tmbrbr, @c01gide, @jndre, and Sepehr Mirzaei.
Pentest Muse is an AI assistant tailored for cybersecurity professionals. It can help penetration testers brainstorm ideas, write payloads, analyze code, and perform reconnaissance. It can also take actions, execute command line codes, and iteratively solve complex tasks.
In addition to this command-line tool, we are excited to introduce the Pentest Muse Web Application! The web app has access to the latest online information, and would be a good AI assistant for your pentesting job.
This tool is intended for legal and ethical use only. It should only be used for authorized security testing and educational purposes. The developers assume no liability and are not responsible for any misuse or damage caused by this program.
requirements.txt
git clone https://github.com/pentestmuse-ai/PentestMuse cd PentestMuse
pip install -r requirements.txt
Install Pentest Muse as a Python Package:
pip install .
In the chat mode, you can chat with pentest muse and ask it to help you brainstorm ideas, write payloads, and analyze code. Run the application with:
python run_app.py
or
pmuse
You can also give Pentest Muse more control by asking it to take actions for you with the agent mode. In this mode, Pentest Muse can help you finish a simple task (e.g., 'help me do sql injection test on url xxx'). To start the program with agent model, you can use:
python run_app.py agent
or
pmuse agent
You can use Pentest Muse with our managed APIs after signing up at www.pentestmuse.ai/signup. After creating an account, you can simply start the pentest muse cli, and the program will prompt you to login.
Alternatively, you can also choose to use your own OpenAI API keys. To do this, you can simply add argument --openai-api-key=[your openai api key] when starting the program.
For any feedback or suggestions regarding Pentest Muse, feel free to reach out to us at contact@pentestmuse.ai or join our discord. Your input is invaluable in helping us improve and evolve.
GTFOcli it's a Command Line Interface for easy binaries search commands that can be used to bypass local security restrictions in misconfigured systems.
Using go:
go install github.com/cmd-tools/gtfocli@latest
Using homebrew:
brew tap cmd-tools/homebrew-tap
brew install gtfocli
Using docker:
docker pull cmdtoolsowner/gtfocli
Search for binary tar:
gtfocli search tar
Search for binary tar from stdin:
echo "tar" | gtfocli search
Search for binaries located into file;
cat myBinaryList.txt
/bin/bash
/bin/sh
tar
arp
/bin/tail
gtfocli search -f myBinaryList.txt
Search for binary Winget.exe:
gtfocli search Winget --os windows
Search for binary Winget from stdin:
echo "Winget" | gtfocli search --os windows
Search for binaries located into file:
cat windowsExecutableList.txt
Winget
c:\\Users\\Desktop\\Ssh
Stordiag
Bash
c:\\Users\\Runonce.exe
Cmdkey
c:\dir\subDir\Users\Certreq.exe
gtfocli search -f windowsExecutableList.txt --os windows
Search for binary Winget and print output in yaml format (see -h for available formats):
gtfocli search Winget -o yaml --os windows
Examples:
Search for binary Winget and print output in yaml format:
docker run -i cmdtoolsowner/gtfocli search Winget -o yaml --os windows
Search for binary tar and print output in json format:
echo 'tar' | docker run -i cmdtoolsowner/gtfocli search -o json
Search for binaries located into file mounted as volume in the container:
cat myBinaryList.txt
/bin/bash
/bin/sh
tar
arp
/bin/tail
docker run -i -v $(pwd):/tmp cmdtoolsowner/gtfocli search -f /tmp/myBinaryList.txt
An example of common use case for gtfocli is together with find:
find / -type f \( -perm 04000 -o -perm -u=s \) -exec gtfocli search {} \; 2>/dev/null
or
find / -type f \( -perm 04000 -o -perm -u=s \) 2>/dev/null | gtfocli search
Thanks to GTFOBins and LOLBAS, without these projects gtfocli would never have come to light.
You want to contribute to this project? Wow, thanks! So please just fork it and send a pull request.
SpeedyTest is a powerful command-line tool for measuring internet speed. With its advanced features and intuitive interface, it provides accurate and comprehensive speed test results. Whether you're a network administrator, developer, or simply want to monitor your internet connection, SpeedyTest is the perfect tool for the job.
git clone https://github.com/HalilDeniz/SpeedyTest.git
Before you can use SpeedyTest, you need to make sure that you have the necessary requirements installed. You can install these requirements by running the following command:
pip install -r requirements.txt
Run the following command to perform a speed test:
python3 speendytest.py
Receiving data \
Speed test completed!
Speed test time: 20.22 second
Server : Farknet - Konya
IP Address: speedtest.farknet.com.tr:8080
Country : Turkey
City : Konya
Ping : 20.41 ms
Download : 90.12 Mbps
Loading : 20 Mbps
Contributions are welcome! To contribute to SpeedyTest, follow these steps:
If you have any questions, comments, or suggestions about PrivacyNet, please feel free to contact me:
SpeedyTest is released under the MIT License. See LICENSE for details.
Mass bruteforce network protocols
Simple personal script to quickly mass bruteforce common services in a large scale of network.
It will check for default credentials on ftp, ssh, mysql, mssql...etc.
This was made for authorized red team penetration testing purpose only.
masscan(faster than nmap) to find alive hosts with common ports from network segment.masscan result.hydra commands to automatically bruteforce supported network services on devices.Kali linux or any preferred linux distributionPython 3.10+# Clone the repo
git clone https://github.com/opabravo/mass-bruter
cd mass-bruter
# Install required tools for the script
apt update && apt install seclists masscan hydraPrivate ip range :
10.0.0.0/8,192.168.0.0/16,172.16.0.0/12
Save masscan results under ./result/masscan/, with the format masscan_<name>.<ext>
Ex: masscan_192.168.0.0-16.txt
Example command:
masscan -p 3306,1433,21,22,23,445,3389,5900,6379,27017,5432,5984,11211,9200,1521 172.16.0.0/12 | tee ./result/masscan/masscan_test.txtExample Resume Command:
masscan --resume paused.conf | tee -a ./result/masscan/masscan_test.txtCommand Options
┌──(root㉿root)-[~/mass-bruter]
└─# python3 mass_bruteforce.py
Usage: [OPTIONS]
Mass Bruteforce Script
Options:
-q, --quick Quick mode (Only brute telnet, ssh, ftp , mysql,
mssql, postgres, oracle)
-a, --all Brute all services(Very Slow)
-s, --show Show result with successful login
-f, --file-path PATH The directory or file that contains masscan result
[default: ./result/masscan/]
--help Show this message and exit.Quick Bruteforce Example:
python3 mass_bruteforce.py -q -f ~/masscan_script.txtFetch cracked credentials:
python3 mass_bruteforce.py -sdpl4hydra
Any contributions are welcomed!
Welcome to CryptChat - where conversations remain truly private. Built on the robust Python ecosystem, our application ensures that every word you send is wrapped in layers of encryption. Whether you're discussing sensitive business details or sharing personal stories, CryptChat provides the sanctuary you need in the digital age. Dive in, and experience the next level of secure messaging!
Clone the repository:
git clone https://github.com/HalilDeniz/CryptoChat.gitNavigate to the project directory:
cd CryptoChatInstall the required dependencies:
pip install -r requirements.txt$ python3 server.py --help
usage: server.py [-h] [--host HOST] [--port PORT]
Start the chat server.
options:
-h, --help show this help message and exit
--host HOST The IP address to bind the server to.
--port PORT The port number to bind the server to.
--------------------------------------------------------------------------
$ python3 client.py --help
usage: client.py [-h] [--host HOST] [--port PORT]
Connect to the chat server.
options:
-h, --help show this help message and exit
--host HOST The server's IP address.
--port PORT The port number of the server.$ python3 serverE.py --help
usage: serverE.py [-h] [--host HOST] [--port PORT] [--key KEY]
Start the chat server.
options:
-h, --help show this help message and exit
--host HOST The IP address to bind the server to. (Default=0.0.0.0)
--port PORT The port number to bind the server to. (Default=12345)
--key KEY The secret key for encryption. (Default=mysecretpassword)
--------------------------------------------------------------------------
$ python3 clientE.py --help
usage: clientE.py [-h] [--host HOST] [--port PORT] [--key KEY]
Connect to the chat server.
options:
-h, --help show this help message and exit
--host HOST The IP address to bind the server to. (Default=127.0.0.1)
--port PORT The port number to bind the server to. (Default=12345)
--key KEY The secret key for encr yption. (Default=mysecretpassword)--help: show this help message and exit--host: The IP address to bind the server.--port: The port number to bind the server.--key : The secret key for encryptionContributions are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.
If you have any questions, comments, or suggestions about CryptChat, please feel free to contact me:
Author:: TW-D
Version:: 1.3.7
Copyright:: Copyright (c) 2022 TW-D
License:: Distributes under the same terms as Ruby
Doc:: https://hak5.github.io/mk7-docs/docs/rest/rest/
Requires:: Ruby >= 2.7.0p0 and Pineapple Mark VII >= 2.1.0-stable
Installation (Debian, Ubuntu, Raspbian)::
sudo apt-get install build-essential curl g++ ruby ruby-dev
sudo gem install net-ssh rest-client tty-progressbar
Library allowing the automation of active or passive attack operations.
Note : "Issues" and "Pull Request" are welcome.
In "./payloads/" directory, you will find :
| COMMAND and CONTROL | Author | Usage |
|---|---|---|
| Hak5 Key Croc - Real-time recovery of keystrokes from a keyboard | TW-D | (edit) ruby ./hak5_key-croc.rb |
| Maltronics WiFi Deauther - Spam beacon frames | TW-D | (edit) ruby ./maltronics_wifi-deauther.rb |
| DEFENSE | Author | Usage |
|---|---|---|
| Hak5 Pineapple Spotter | TW-D with special thanks to @DrSKiZZ, @cribb-it, @barry99705 and @dark_pyrro | (edit) ruby ./hak5-pineapple_spotter.rb |
| DoS | Author | Usage |
|---|---|---|
| Deauthentication of clients available on the access points | TW-D | (edit) ruby ./deauthentication-clients.rb |
| EXPLOITATION | Author | Usage |
|---|---|---|
| Evil WPA Access Point | TW-D | (edit) ruby ./evil-wpa_access-point.rb |
| Fake Access Points | TW-D | (edit) ruby ./fake_access-points.rb |
| Mass Handshakes | TW-D | (edit) ruby ./mass-handshakes.rb |
| Rogue Access Points | TW-D | (edit) ruby ./rogue_access-points.rb |
| Twin Access Points | TW-D | (edit) ruby ./twin_access-points.rb |
| GENERAL | Author | Usage |
|---|---|---|
| System Status, Disk Usage, ... | TW-D | (edit) ruby ./dashboard-stats.rb |
| Networking Interfaces | TW-D | (edit) ruby ./networking-interfaces.rb |
| System Logs | TW-D | (edit) ruby ./system-logs.rb |
| RECON | Author | Usage |
|---|---|---|
| Access Points and Clients on 2.4GHz and 5GHz (with a supported adapter) | TW-D | (edit) ruby ./access-points_clients_5ghz.rb |
| Access Points and Clients | TW-D | (edit) ruby ./access-points_clients.rb |
| MAC Addresses of Access Points | TW-D | (edit) ruby ./access-points_mac-addresses.rb |
| Tagged Parameters of Access Points | TW-D | (edit) ruby ./access-points_tagged-parameters.rb |
| Access Points and Wireless Network Mapping with WiGLE | TW-D | (edit) ruby ./access-points_wigle.rb |
| MAC Addresses of Clients | TW-D | (edit) ruby ./clients_mac-addresses.rb |
| OPEN Access Points | TW-D | (edit) ruby ./open_access-points.rb |
| WEP Access Points | TW-D | (edit) ruby ./wep_access-points.rb |
| WPA Access Points | TW-D | (edit) ruby ./wpa_access-points.rb |
| WPA2 Access Points | TW-D | (edit) ruby ./wpa2_access-points.rb |
| WPA3 Access Points | TW-D | (edit) ruby ./wpa3_access-points.rb |
| WARDRIVING | Author | Usage |
|---|---|---|
| Continuous Recon on 2.4GHz and 5GHz (with a supported adapter) | TW-D | (edit) ruby ./continuous-recon_5ghz.rb [CTRL+c] |
| Continuous Recon for Handshakes Capture | TW-D | (edit) ruby ./continuous-recon_handshakes.rb [CTRL+c] |
| Continuous Recon | TW-D | (edit) ruby ./continuous-recon.rb [CTRL+c] |
#
# Title: <TITLE>
#
# Description: <DESCRIPTION>
#
#
# Author: <AUTHOR>
# Version: <VERSION>
# Category: <CATEGORY>
#
# STATUS
# ======================
# <SHORT-DESCRIPTION> ... SETUP
# <SHORT-DESCRIPTION> ... ATTACK
# <SHORT-DESCRIPTION> ... SPECIAL
# <SHORT-DESCRIPTION> ... FINISH
# <SHORT-DESCRIPTION> ... CLEANUP
# <SHORT-DESCRIPTION> ... OFF
#
require_relative('<PATH-TO>/classes/PineappleMK7.rb')
system_authentication = PineappleMK7::System::Authentication.new
system_authentication.host = "<PINEAPPLE-IP-ADDRESS>"
system_authentication.port = 1471
system_authentication.mac = "<PINEAPPLE-MAC-ADDRESS>"
system_authentication.password = "<ROOT-ACCOUNT-PASSWORD>"
if (system_authentication.login)
led = PineappleMK7::System::LED.new
# SETUP
#
led.setup
#
# [...]
#
# ATTACK
#
led.attack
#
# [...]
#
# SPECIAL
#
led.special
#
# [...]
#
# FINISH
#
led.finish
#
# [...]
#
# CLEANUP
#
led.cleanup
#
# [...]
#
# OFF
#
led.off
endNote : Don't hesitate to take inspiration from the payloads directory.
system_authentication = PineappleMK7::System::Authentication.new
system_authentication.host = (string) "<PINEAPPLE-IP-ADDRESS>"
system_authentication.port = (integer) 1471
system_authentication.mac = (string) "<PINEAPPLE-MAC-ADDRESS>"
system_authentication.password = (string) "<ROOT-ACCOUNT-PASSWORD>"
system_authentication.login()led = PineappleMK7::System::LED.new
led.setup()
led.failed()
led.attack()
led.special()
led.cleanup()
led.finish()
led.off()dashboard_notifications = PineappleMK7::Modules::Dashboard::Notifications.new
dashboard_notifications.clear()dashboard_stats = PineappleMK7::Modules::Dashboard::Stats.new
dashboard_stats.output()logging_system = PineappleMK7::Modules::Logging::System.new
logging_system.output()pineap_clients = PineappleMK7::Modules::PineAP::Clients.new
pineap_clients.connected_clients()
pineap_clients.previous_clients()
pineap_clients.kick( (string) mac )
pineap_clients.clear_previous()evil_wpa = PineappleMK7::Modules::PineAP::EvilWPA.new
evil_wpa.ssid = (string default:'PineAP_WPA')
evil_wpa.bssid = (string default:'00:13:37:BE:EF:00')
evil_wpa.auth = (string default:'psk2+ccmp')
evil_wpa.password = (string default:'pineapplesareyummy')
evil_wpa.hidden = (boolean default:false)
evil_wpa.enabled = (boolean default:false)
evil_wpa.capture_handshakes = (boolean default:false)
evil_wpa.save()pineap_filtering = PineappleMK7::Modules::PineAP::Filtering.new
pineap_filtering.client_filter( (string) 'allow' | 'deny' )
pineap_filtering.add_client( (string) mac )
pineap_filtering.clear_clients()
pineap_filtering.ssid_filter( (string) 'allow' | 'deny' )pineap_impersonation = PineappleMK7::Modules::PineAP::Impersonation.new
pineap_impersonation.output()
pineap_impersonation.add_ssid( (string) ssid )
pineap_impersonation.clear_pool()open_ap = PineappleMK7::Modules::PineAP::OpenAP.new
open_ap.output()pineap_settings = PineappleMK7::Modules::PineAP::Settings.new
pineap_settings.enablePineAP = (boolean default:true)
pineap_settings.autostartPineAP = (boolean default:true)
pineap_settings.armedPineAP = (boolean default:false)
pineap_settings.ap_channel = (string default:'11')
pineap_settings.karma = (boolean default:false)
pineap_settings.logging = (boolean default:false)
pineap_settings.connect_notifications = (boolean default:false)
pineap_settings.disconnect_notifications = (boolean default:false)
pineap_settings.capture_ssids = (boolean default:false)
pineap_settings.beacon_responses = (boolean default:false)
pineap_settings.broadcast_ssid_pool = (boolean default:false)
pineap_settings.broadcast_ssid_pool_random = (boolean default:false)
pineap_settings.pineap_mac = (string default:system_authentication.mac)
pineap_settings.target_mac = (string default:'FF:FF:FF:FF:FF:FF')< br/>pineap_settings.beacon_response_interval = (string default:'NORMAL')
pineap_settings.beacon_interval = (string default:'NORMAL')
pineap_settings.save()recon_handshakes = PineappleMK7::Modules::Recon::Handshakes.new
recon_handshakes.start( (object) ap )
recon_handshakes.stop()
recon_handshakes.output()
recon_handshakes.download( (object) handshake, (string) destination )
recon_handshakes.clear()recon_scanning = PineappleMK7::Modules::Recon::Scanning.new
recon_scanning.start( (integer) scan_time )
recon_scanning.start_continuous( (boolean) autoHandshake )
recon_scanning.stop_continuous()
recon_scanning.output( (integer) scanID )
recon_scanning.tags( (object) ap )
recon_scanning.deauth_ap( (object) ap )
recon_scanning.delete( (integer) scanID )settings_networking = PineappleMK7::Modules::Settings::Networking.new
settings_networking.interfaces()
settings_networking.client_scan( (string) interface )
settings_networking.client_connect( (object) network, (string) interface )
settings_networking.client_disconnect( (string) interface )
settings_networking.recon_interface( (string) interface )
Welcome to HackBot, an AI-powered cybersecurity chatbot designed to provide helpful and accurate answers to your cybersecurity-related queries and also do code analysis and scan analysis. Whether you are a security researcher, an ethical hacker, or just curious about cybersecurity, HackBot is here to assist you in finding the information you need.
HackBot utilizes the powerful language model Meta-LLama2 through the "LlamaCpp" library. This allows HackBot to respond to your questions in a coherent and relevant manner. Please make sure to keep your queries in English and adhere to the guidelines provided to get the best results from HackBot.
Before you proceed with the installation, ensure you have the following prerequisites:
pip package managerVisual studio Code - Follow the steps in this link llama-cpp-prereq-install-instructions
cmakegit clone https://github.com/morpheuslord/hackbot.git
cd hackbotpip install -r requirements.txtpython hackbot.pyThe first time you run HackBot, it will check for the AI model required for the chatbot. If the model is not present, it will be automatically downloaded and saved as "llama-2-7b-chat.ggmlv3.q4_0.bin" in the project directory.
To start a conversation with HackBot, run the following command:
python hackbot.pyHackBot will display a banner and wait for your input. You can ask cybersecurity-related questions, and HackBot will respond with informative answers. To exit the chat, simply type "quit_bot" in the input prompt.
Here are some additional commands you can use:
clear_screen: Clears the console screen for better readability.quit_bot: This is used to quit the chat applicationbot_banner: Prints the default bots banner.contact_dev: Provides my contact information.save_chat: Saves the current sessions interactions.vuln_analysis: Does a Vuln analysis using the scan data or log file.static_code_analysis: Does a Static code analysis using the scan data or log file.Note: I am working on more addons and more such commands to give a more chatGPT experience
Please Note: HackBot's responses are based on the Meta-LLama2 AI model, and its accuracy depends on the quality of the queries and data provided to it.
I am also working on AI training by which I can teach it how to be more accurately tuned to work for hackers on a much more professional level.
We welcome contributions to improve HackBot's functionality and accuracy. If you encounter any issues or have suggestions for enhancements, please feel free to open an issue or submit a pull request. Follow these steps to contribute:
main branch of this repository.Please maintain a clean commit history and adhere to the project's coding guidelines.
If anyone with the know-how of training text generation models can help improve the code.
For any questions, feedback, or inquiries related to HackBot, feel free to contact the project maintainer:
John Clifton Davies, a convicted fraudster estimated to have bilked dozens of technology startups out of more than $30 million through phony investment schemes, has a brand new pair of scam companies that are busy dashing startup dreams: A fake investment firm called Equity-Invest[.]ch, and Diligere[.]co.uk, a scam due diligence company that Equity-Invest insists all investment partners use.
A native of the United Kingdom, Mr. Davies absconded from justice before being convicted on multiple counts of fraud in 2015. Prior to his conviction, Davies served 16 months in jail before being cleared on suspicion of murdering his third wife on their honeymoon in India.
The scam artist John Bernard (left) in a recent Zoom call, and a photo of John Clifton Davies from 2015.
John Clifton Davies was convicted in 2015 of swindling businesses throughout the U.K. that were struggling financially and seeking to restructure their debt. For roughly six years, Davies ran a series of firms that pretended to offer insolvency services. Instead, he simply siphoned what little remaining money these companies had, spending the stolen funds on lavish cars, home furnishings, vacations and luxury watches.
In a three-part series published in 2020, KrebsOnSecurity exposed how Davies — wanted by authorities in the U.K. — had fled the country, taken on the surname Bernard, remarried, and moved to his new (and fourth) wife’s hometown in Ukraine.
After eluding justice in the U.K., Davies reinvented himself as The Private Office of John Bernard, pretending to be a billionaire Swiss investor who made his fortunes in the dot-com boom 20 years ago and who was seeking private equity investment opportunities.
In case after case, Bernard would promise to invest millions in hi-tech startups, only to insist that companies pay tens of thousands of dollars worth of due diligence fees up front. However, the due diligence company he insisted on using — another Swiss firm called The Inside Knowledge — also was secretly owned by Bernard, who would invariably pull out of the deal after receiving the due diligence money.
Bernard found a constant stream of new marks by offering extraordinarily generous finders fees to investment brokers who could introduce him to companies seeking an infusion of cash. Inside Knowledge and The Private Office both closed up shop not long after being exposed here in 2020.
In April 2023, KrebsOnSecurity wrote about Codes2You, a recent Davies venture which purports to be a “full cycle software development company” based in the U.K. The company’s website no longer lists any of Davies’ known associates, but the site does still reference software and cloud services tied to those associates — including MySolve, a “multi-feature platform for insolvency practitioners.”
Earlier this month, KrebsOnSecurity heard from an investment broker who found out his client had paid more than $50,000 in due diligence fees related to a supposed multi-million dollar investment offer from a Swiss concern called Equity-Invest[.]ch.
The investment broker, who spoke on condition that neither he nor his client be named, said Equity-Invest began getting cold feet after his client plunked down the due diligence fees.
“Things started to go sideways when the investor purportedly booked a trip to the US to meet the team but canceled last minute because ‘his pregnant wife got in a car accident,'” the broker explained. “After that, he was radio silent until the contract expired.”
The broker said he grew suspicious when he learned that the Equity-Invest domain name was less than six months old. The broker’s suspicions were confirmed after he discovered the due diligence company that Equity-Invest insisted on using — Diligere[.]co.uk — included an email address on its homepage for another entity called Ardelis Solutions.
A corporate entity in the UK called Ardelis Solutions was key to showing the connection to Davies’ former scam investment and due diligence firms in the Codes2You investigation published earlier this year.
Although Diligere’s website claims the due diligence firm has “13 years of experiance” [sic], its domain name was only registered in April 2023. What’s more, virtually all of the vapid corporate-speak published on Diligere’s homepage is identical to text on the now-defunct InsideKnowledge[.]ch — the fake due diligence firm secretly owned for many years by The Private Office of John Bernard (John Clifton Davies).
A snippet of text from the now-defunct website of the fake Swiss investor John Bernard, in real life John Clifton Davies.
“Our steadfast conviction and energy for results is what makes us stand out,” both sites state. “We care for our clients’ and their businesses, we share their ambitions and align our goals to complement their objectives. Our clients know we’re in this together. We work in close partnership with our clients to deliver palpable results regardless of geography, complexity or controversy.”
The copy on Diligere’s homepage is identical to that once on Insideknowledge[.]com, a phony due diligence company run by John Clifton Davies.
python3 based multi clients reverse shell.
1. Don't Upload Any Payloads To VirusTotal.com Bcz This tool will not work
with Time.
2. Virustotal Share Signatures With AV Comapnies.
3. Again Don't be an Idiot!
1. git clone https://github.com/machine1337/pyFUD
2. python3 server.py (enter your ip,port and start the server)
3. client.py (Edit IP AND PORT To Put Your Own IP,Port)
1. python3 server.py
2. Now Compile client.py to exe (make sure change ip and port in it)
1. Very Simple And Fully Undectable Reverse Shell
2. Multi Client Handling
3. Persistent Shell
3. auto-reconnect
5. U can Convert client.py to exe using pyinstaller tool in windows.
Use this tool Only for Educational Purpose And I will Not be Responsible For ur cruel act.
John Clifton Davies, a 60-year-old con man from the United Kingdom who fled the country in 2015 before being sentenced to 12 years in prison for fraud, has enjoyed a successful life abroad swindling technology startups by pretending to be a billionaire investor. Davies’ newest invention appears to be “CodesToYou,” which purports to be a “full cycle software development company” based in the U.K.
The scam artist John Bernard a.k.a. Alan John Mykailov (left) in a recent Zoom call, and a mugshot of John Clifton Davies from nearly a decade earlier.
Several articles here have delved into the history of John Bernard, the pseudonym used by a fake billionaire technology investor who tricked dozens of startups into giving him tens of millions of dollars.
John Bernard’s real name is John Clifton Davies, a convicted fraudster from the United Kingdom who is currently a fugitive from justice. For several years until reinventing himself again quite recently, Bernard pretended to be a billionaire Swiss investor who made his fortunes in the dot-com boom 20 years ago.
“The Private Office of John Bernard” let it be known to investment brokers that he had tens of millions of dollars to invest in tech startups, and he attracted a stream of new victims by offering extraordinarily generous finder’s fees to brokers who helped him secure new clients. But those brokers would eventually get stiffed because Bernard’s company would never consummate a deal.
John Bernard’s former website, where he pretended to be a billionaire tech investor.
Bernard would promise to invest millions in tech startups, and then insist that companies pay tens of thousands of dollars worth of due diligence fees up front. However, the due diligence company he insisted on using — another Swiss firm called The Inside Knowledge GmbH — also was secretly owned by Bernard, who would invariably pull out of the deal after receiving the due diligence money.
A variety of clues suggest Davies has recently adopted at least one other identity — Alan John Mykhailov — who is listed as chairman of a British concern called CodesToYou LTD, incorporated in May 2022. The CodesToYou website says the company employs talented coders in several countries, and that its programmers offer “your ultimate balance between speed, cost and quality.”
The team from CodesToYou.
In response to questions from KrebsOnSecurity, CodesToYou’s marketing manager — who gave their name only as “Zhena” — said the company was not affiliated with any John Bernard or John Clifton Davies, and maintained that CodesToYou is a legitimate enterprise.
But publicly available information about this company and its leadership suggests otherwise. Official incorporation documents from the U.K.’s Companies House represent that CodesToYou is headed by an Alan John Mykhailov, a British citizen born in March 1958.
Companies House says Mykhailov is an officer in three other companies, including one called Blackstone Corporate Alliance Ltd. According to the Swiss business tracking service business-monitor.ch, Blackstone Corporate Alliance Ltd. is currently the entity holding a decision-making role in John Bernard’s fake due diligence company — The Inside Knowledge GmbH — which is now in liquidation.
A screen shot of the stock photos and corporate-speak on John Bernard’s old website. Image: Archive.org
Also listed as a partner in Blackstone Corporate Alliance Limited is Igor Hubskyi (a.k.a. Igor Gubskyi), a Ukrainian man who was previously president of The Inside Knowledge GmbH.
The CodesToYou website says the company’s marketing team lead is Maria Yakovleva, and the photo of this employee matches the profile for the LinkedIn account name “Maria Y.” That same LinkedIn profile and photo previously listed Maria by a different first and last name — Mariya Kulikova; back then, Ms. Kulikova’s LinkedIn profile said she was an executive assistant in The Private Office of Mr. John Bernard.
Companies House lists Alan John Mykhailov as a current officer in two other companies, including Frisor Limited, and Ardelis Solutions Limited. A cached copy of the now-defunct Ardelis Solutions website says it was a private equity firm.
CodesToYou’s Maria also included Ardelis Solutions in the work history section of her LinkedIn resume. That is, until being contacted by this author on LinkedIn, after which Maria’s profile picture and any mention of Ardelis Solutions were deleted.
Listed as head of business development at CodesToYou is David Bruno, a Canadian man whose LinkedIn profile says he is founder of an organization called “World Privacy Resource.” As KrebsOnSecurity reported in 2020, Bruno was at the time promoting himself as the co-CEO of a company called SafeSwiss Secure Communication AG, and the founder of another tech startup called Secure Swiss Data.
Secure Swiss Data’s domain — secureswissdata.com — is a Swiss concern that sells encrypted email and data services. According to DomainTools.com, that website name was registered in 2015 by The Inside Knowledge GmbH. In February 2020, a press release announced that Secure Swiss Data was purchased in an “undisclosed multimillion buyout” by SafeSwiss Secure Communication AG.
A cached copy of the Ardelis Solutions website, which said it was a private equity firm and included similar stock images as John Bernard’s investment website.
When reached in 2020 and asked about his relationship to Mr. Bernard, Mr. Bruno said the two were business partners and that he couldn’t imagine that Mr. Bernard would be involved in anything improper. To this day Mr. Bruno is the only person I’ve spoken to who has had anything positive to say about Mr. Bernard.
Mr. Bruno did not respond to requests for comment this time around, but his LinkedIn profile no longer makes any mention of Secure Swiss Data or SafeSwiss — both companies he claimed to run for many years. Nor does it mention CodesToYou. However, Mr. Bruno’s former company SafeSwiss is listed as one of the six “portfolio” companies whose services are promoted on the CodesToYou website.
In mid-2021, Bruno announced he was running for public office in Ontario.
“The Kenora resident is no stranger to the government as he contributed to Canada’s new Digital Charter, Bill C-11, which is a new Cyber Security policy,” reported Drydennow.com, a news website that covers Northwestern Ontario. Drydennow says the next federal election is expected to be held on or before Oct. 16, 2023.
John Clifton Davies was convicted in 2015 of swindling businesses throughout the U.K. that were struggling financially and seeking to restructure their debt. For roughly six years, Davies ran a series of firms that pretended to offer insolvency services, but instead simply siphoned what little remaining money these companies had.
The very first entity mentioned in the technology portfolio advertised on the CodesToYou website is called “MySolve,” and it purports to offer a “multi-feature platform for insolvency practitioners.”
Mr. Davies’ fourth wife, Iryna Davies, is listed as a director of one of the insolvency consulting businesses in the U.K. that was part of John Davies’ 2015 fraud conviction. Prior to his trial for fraud, Davies served 16 months in jail before being cleared of murdering his third wife on their honeymoon in India: Colette Davies, 39, died after falling 80 feet from a viewing point at a steep gorge in the Himachal Pradesh region of India.
Mr. Davies was charged with murder and fraud after he attempted to collect GBP 132,000 in her life insurance payout, but British prosecutors ultimately conceded they did not have enough evidence to convict him.
The scams favored by Davies and his alter egos are smart because he never approaches investors directly; rather, investors are incentivized to put his portfolio in front of tech firms seeking financial backing. And all the best cons begin as an idea or possibility planted in the target’s mind.
It’s also a reliable scam because companies bilked by small-time investment schemes rarely pursue legal action, mainly because the legal fees involved can quickly surpass the losses. On top of that, many victims will likely be too ashamed to admit their duping. Victims who do press their case in court and win then face the daunting challenge of collecting damages from a slew of ephemeral shell corporations.
The latest Bernard victim to speak publicly — a Norwegian company hoping to build a fleet of environmentally friendly shipping vessels — is now embroiled in a lawsuit over a deal gone bad. As part of that scam, Bernard falsely claimed to have secured $100 million from six other wealthy investors, including the founder of Uber and the artist Abel Makkonen Tesfaye, better known as The Weeknd.
If you liked this story, check out my previous reporting on John Bernard/Davies:
Due Diligence That Money Can’t Buy
Who is Tech Investor John Bernard?
Promising Infusions of Cash, Fake Investor John Bernard Walked Away With $30 Million
Investment Scammer John Davies Reinvents Himself?
Fake Investor John Bernard Sinks Norwegian Green Shipping Dreams
Article tags
