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Chaos - Origin IP Scanning Utility Developed With ChatGPT

By: Zion3R


chaos is an 'origin' IP scanner developed by RST in collaboration with ChatGPT. It is a niche utility with an intended audience of mostly penetration testers and bug hunters.

An origin-IP is a term-of-art expression describing the final public IP destination for websites that are publicly served via 3rd parties. If you'd like to understand more about why anyone might be interested in Origin-IPs, please check out our blog post.

chaos was rapidly prototyped from idea to functional proof-of-concept in less than 24 hours using our principles of DevOps with ChatGPT.

usage: chaos.py [-h] -f FQDN -i IP [-a AGENT] [-C] [-D] [-j JITTER] [-o OUTPUT] [-p PORTS] [-P] [-r] [-s SLEEP] [-t TIMEOUT] [-T] [-v] [-x] 
_..._
.-'` `'-.
__|___________|__
\ /
`._ CHAOS _.'
`-------`
/ \\
/ \\
/ \\
/ \\
/ \\
/ \\
/ \\
/ \\
/ \\
/_____________________\\
CHAtgpt Origin-ip Scanner
_______ _______ _______ _______ _______
|\\ /|\\ /|\\ /|\\ /|\\/|
| +---+ | +---+ | +---+ | +---+ | +---+ |
| |H | | |U | | |M | | |A | | |N | |
| |U | | |S | | |A | | |N | | |C | |
| |M | | |E | | |N | | |D | | |O | |
| |A | | |R | | |C | | | | | |L | |
| +---+ | +---+ | +---+ | +---+ | +---+ |
|/_____|\\_____|\\_____|\\_____|\\_____\\

Origin IP Scanner developed with ChatGPT
cha*os (n): complete disorder and confusion
(ver: 0.9.4)


Features

  • Threaded for performance gains
  • Real-time status updates and progress bars, nice for large scans ;)
  • Flexible user options for various scenarios & constraints
  • Dataset reduction for improved scan times
  • Easy to use CSV output

Installation

  1. Download / clone / unzip / whatever
  2. cd path/to/chaos
  3. pip3 install -U pip setuptools virtualenv
  4. virtualenv env
  5. source env/bin/activate
  6. (env) pip3 install -U -r ./requirements.txt
  7. (env) ./chaos.py -h

Options

-h, --help            show this help message and exit
-f FQDN, --fqdn FQDN Path to FQDN file (one FQDN per line)
-i IP, --ip IP IP address(es) for HTTP requests (Comma-separated IPs, IP networks, and/or files with IP/network per line)
-a AGENT, --agent AGENT
User-Agent header value for requests
-C, --csv Append CSV output to OUTPUT_FILE.csv
-D, --dns Perform fwd/rev DNS lookups on FQDN/IP values prior to request; no impact to testing queue
-j JITTER, --jitter JITTER
Add a 0-N second randomized delay to the sleep value
-o OUTPUT, --output OUTPUT
Append console output to FILE
-p PORTS, --ports PORTS
Comma-separated list of TCP ports to use (default: "80,443")
-P, --no-prep Do not pre-scan each IP/port w ith `GET /` using `Host: {IP:Port}` header to eliminate unresponsive hosts
-r, --randomize Randomize(ish) the order IPs/ports are tested
-s SLEEP, --sleep SLEEP
Add N seconds before thread completes
-t TIMEOUT, --timeout TIMEOUT
Wait N seconds for an unresponsive host
-T, --test Test-mode; don't send requests
-v, --verbose Enable verbose output
-x, --singlethread Single threaded execution; for 1-2 core systems; default threads=(cores-1) if cores>2

Examples

Localhost Testing

Launch python HTTP server

% python3 -u -m http.server 8001
Serving HTTP on :: port 8001 (http://[::]:8001/) ...

Launch ncat as HTTP on a port detected as SSL; use a loop because --keep-open can hang

% while true; do ncat -lvp 8443 -c 'printf "HTTP/1.0 204 Plaintext OK\n\n<html></html>\n"'; done
Ncat: Version 7.94 ( https://nmap.org/ncat )
Ncat: Listening on [::]:8443
Ncat: Listening on 0.0.0.0:8443

Also launch ncat as SSL on a port that will default to HTTP detection

% while true; do ncat --ssl -lvp 8444 -c 'printf "HTTP/1.0 202 OK\n\n<html></html>\n"'; done    
Ncat: Version 7.94 ( https://nmap.org/ncat )
Ncat: Generating a temporary 2048-bit RSA key. Use --ssl-key and --ssl-cert to use a permanent one.
Ncat: SHA-1 fingerprint: 0208 1991 FA0D 65F0 608A 9DAB A793 78CB A6EC 27B8
Ncat: Listening on [::]:8444
Ncat: Listening on 0.0.0.0:8444

Prepare an FQDN file:

% cat ../test_localhost_fqdn.txt 
www.example.com
localhost.example.com
localhost.local
localhost
notreally.arealdomain

Prepare an IP file / list:

% cat ../test_localhost_ips.txt 
127.0.0.1
127.0.0.0/29
not_an_ip_addr
-6.a
=4.2
::1

Run the scan

  • Note an IPv6 network added to IPs on the CLI
  • -p to specify the ports we are listening on
  • -x for single threaded run to give our ncat servers time to restart
  • -s0.2 short sleep for our ncat servers to restart
  • -t1 to timeout after 1 second
% ./chaos.py -f ../test_localhost_fqdn.txt -i ../test_localhost_ips.txt,::1/126 -p 8001,8443,8444 -x -s0.2 -t1   
2023-06-21 12:48:33 [WARN] Ignoring invalid FQDN value: localhost.local
2023-06-21 12:48:33 [WARN] Ignoring invalid FQDN value: localhost
2023-06-21 12:48:33 [WARN] Ignoring invalid FQDN value: notreally.arealdomain
2023-06-21 12:48:33 [WARN] Error: invalid IP address or CIDR block =4.2
2023-06-21 12:48:33 [WARN] Error: invalid IP address or CIDR block -6.a
2023-06-21 12:48:33 [WARN] Error: invalid IP address or CIDR block not_an_ip_addr
2023-06-21 12:48:33 [INFO] * ---- <META> ---- *
2023-06-21 12:48:33 [INFO] * Version: 0.9.4
2023-06-21 12:48:33 [INFO] * FQDN file: ../test_localhost_fqdn.txt
2023-06-21 12:48:33 [INFO] * FQDNs loaded: ['www.example.com', 'localhost.example.com']
2023-06-21 12:48:33 [INFO] * IP input value(s): ../test_localhost_ips.txt,::1/126
2023-06-21 12:48:33 [INFO] * Addresses pars ed from IP inputs: 12
2023-06-21 12:48:33 [INFO] * Port(s): 8001,8443,8444
2023-06-21 12:48:33 [INFO] * Thread(s): 1
2023-06-21 12:48:33 [INFO] * Sleep value: 0.2
2023-06-21 12:48:33 [INFO] * Timeout: 1.0
2023-06-21 12:48:33 [INFO] * User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.80 Safari/537.36 ch4*0s/0.9.4
2023-06-21 12:48:33 [INFO] * ---- </META> ---- *
2023-06-21 12:48:33 [INFO] 36 unique address/port addresses for testing
Prep Tests: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ&# 9608;โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 36/36 [00:29<00:00, 1.20it/s]
2023-06-21 12:49:03 [INFO] 9 IP/ports verified, reducing test dataset from 72 entries
2023-06-21 12:49:03 [INFO] 18 pending tests remain after pre-testing
2023-06-21 12:49:03 [INFO] Queuing 18 threads
++RCVD++ (200 OK) www.example.com @ :::8001
++RCVD++ (204 Plaintext OK) www.example.com @ :::8443
++RCVD++ (202 OK) www.example.com @ :::8444
++RCVD++ (200 OK) www.example.com @ ::1:8001
++RCVD++ (204 Plaintext OK) www.example.com @ ::1:8443
++RCVD++ (202 OK) www.example.com @ ::1:8444
++RCVD++ (200 OK) www.example.com @ 127.0.0.1:8001
++RCVD++ (204 Plaintext OK) www.example.com @ 127.0.0.1:8443
++RCVD++ (202 OK) www.example.com @ 127.0.0.1:8444
++RCVD++ (200 OK) localhost.example.com @ :::8001
++RCVD++ (204 Plaintext OK) localhost.example.com @ :::8443
++RCVD+ + (202 OK) localhost.example.com @ :::8444
++RCVD++ (200 OK) localhost.example.com @ ::1:8001
++RCVD++ (204 Plaintext OK) localhost.example.com @ ::1:8443
++RCVD++ (202 OK) localhost.example.com @ ::1:8444
++RCVD++ (200 OK) localhost.example.com @ 127.0.0.1:8001
++RCVD++ (204 Plaintext OK) localhost.example.com @ 127.0.0.1:8443
++RCVD++ (202 OK) localhost.example.com @ 127.0.0.1:8444
Origin Scan: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ&#96 08;โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 18/18 [00:06<00:00, 2.76it/s]
2023-06-21 12:49:09 [RSLT] Results from 5 FQDNs:
::1
::1:8444 => (202 / OK)
::1:8443 => (204 / Plaintext OK)
::1:8001 => (200 / OK)

127.0.0.1
127.0.0.1:8001 => (200 / OK)
127.0.0.1:8443 => (204 / Plaintext OK)
127.0.0.1:8444 => (202 / OK)

::
:::8001 => (200 / OK)
:::8443 => (204 / Plaintext OK)
:::8444 => (202 / OK)

www.example.com
:::8001 => (200 / OK)
:::8443 => (204 / Plaintext OK)
:::8444 => (202 / OK)
::1:8001 => (200 / OK)
::1:8443 => (204 / Plaintext OK)
::1:8444 => (202 / OK)
127.0.0.1:8001 => (200 / OK)
127.0.0.1:8443 => (204 / Plaintext OK)
127.0.0.1:8444 => (202 / OK)

localhost.example.com
:::8001 => (200 / OK)
:::8443 => (204 / Plaintext OK)
:::8444 => (202 / OK)
::1:8001 => (200 / OK)
::1:8443 => (204 / Plaintext OK)
::1:8444 => (202 / OK)
127.0.0.1:8001 => (200 / OK)
127.0.0.1:8443 => (204 / Plaintext OK)
127.0.0.1:8444 => (202 / OK)


rst@r57 chaos %

Test & Verbose localhost

-T runs in test mode (do everything except send requests)

-v verbose option provides additional output


Known Defects

  • HTTP/HTTPS detection is not ideal
  • Need option to adjust CSV newline delimiter
  • Need options to adjust where long strings / many lines are truncated
  • Try to figure out why we marked requests v2.x as required ;)
  • Options for very-verbose / quiet
  • Stagger thread launch when we're using sleep / jitter
  • Search for meta-refresh in 200 responses
  • Content-Location header for 201s ?
  • Improve thread name generation so we have the right number of unique names
  • Sanity check on IPv6 netmasks to prevent scans that outlive the sun?
  • TBD?

Related Links

Disclaimers

  • Copyright (C) 2023 RST
  • This software is distributed on an "AS IS" basis, without express or implied warranties of any kind
  • This software is intended for research and/or authorized testing; it is your responsibility to ensure you are authorized to use this software in any way
  • By using this software you acknowledge that you are responsible for your actions and assume all liability for any direct, indirect, or other damages


ReconAIzer - A Burp Suite Extension To Add OpenAI (GPT) On Burp And Help You With Your Bug Bounty Recon To Discover Endpoints, Params, URLs, Subdomains And More!

By: Zion3R


ReconAIzer is a powerful Jython extension for Burp Suite that leverages OpenAI to help bug bounty hunters optimize their recon process. This extension automates various tasks, making it easier and faster for security researchers to identify and exploit vulnerabilities.

Once installed, ReconAIzer add a contextual menu and a dedicated tab to see the results:


Prerequisites

  • Burp Suite
  • Jython Standalone Jar

Installation

Follow these steps to install the ReconAIzer extension on Burp Suite:

Step 1: Download Jython

  1. Download the latest Jython Standalone Jar from the official website: https://www.jython.org/download
  2. Save the Jython Standalone Jar file in a convenient location on your computer.

Step 2: Configure Jython in Burp Suite

  1. Open Burp Suite.
  2. Go to the "Extensions" tab.
  3. Click on the "Extensions settings" sub-tab.
  4. Under "Python Environment," click on the "Select file..." button next to "Location of the Jython standalone JAR file."
  5. Browse to the location where you saved the Jython Standalone Jar file in Step 1 and select it.
  6. Wait for the "Python Environment" status to change to "Jython (version x.x.x) successfully loaded," where x.x.x represents the Jython version.

Step 3: Download and Install ReconAIzer

  1. Download the latest release of ReconAIzer
  2. Open Burp Suite
  3. Go back to the "Extensions" tab in Burp Suite.
  4. Click the "Add" button.
  5. In the "Add extension" dialog, select "Python" as the "Extension type."
  6. Click on the "Select file..." button next to "Extension file" and browse to the location where you saved the ReconAIzer.py file in Step 3.1. Select the file and click "Open."
  7. Make sure the "Load" checkbox is selected and click the "Next" button.
  8. Wait for the extension to be loaded. You should see a message in the "Output" section stating that the ReconAIzer extension has been successfully loaded.

Congratulations! You have successfully installed the ReconAIzer extension in Burp Suite. You can now start using it to enhance your bug bounty hunting experience.

Once it's done, you must configure your OpenAI API key on the "Config" tab under "ReconAIzer" tab.

Feel free to suggest prompts improvements or anything you would like to see on ReconAIzer!

Happy bug hunting!



GPT_Vuln-analyzer - Uses ChatGPT API And Python-Nmap Module To Use The GPT3 Model To Create Vulnerability Reports Based On Nmap Scan Data


This is a Proof Of Concept application that demostrates how AI can be used to generate accurate results for vulnerability analysis and also allows further utilization of the already super useful ChatGPT.

Requirements

  • Python 3.10
  • All the packages mentioned in the requirements.txt file
  • OpenAi api

Usage

  • First Change the "API__KEY" part of the code with OpenAI api key
openai.api_key = "__API__KEY" # Enter your API key
  • second install the packages
pip3 install -r requirements.txt
or
pip install -r requirements.txt
  • run the code python3 gpt_vuln.py <> or if windows run python gpt_vuln.py <>

Supported in both windows and linux

Understanding the code

Profiles:

Parameter Return data Description Nmap Command
p1 json Effective Scan -Pn -sV -T4 -O -F
p2 json Simple Scan -Pn -T4 -A -v
p3 json Low Power Scan -Pn -sS -sU -T4 -A -v
p4 json Partial Intense Scan -Pn -p- -T4 -A -v
p5 json Complete Intense Scan -Pn -sS -sU -T4 -A -PE -PP -PS80,443 -PA3389 -PU40125 -PY -g 53 --script=vuln

The profile is the type of scan that will be executed by the nmap subprocess. The Ip or target will be provided via argparse. At first the custom nmap scan is run which has all the curcial arguments for the scan to continue. nextly the scan data is extracted from the huge pile of data which has been driven by nmap. the "scan" object has a list of sub data under "tcp" each labled according to the ports opened. once the data is extracted the data is sent to openai API davenci model via a prompt. the prompt specifically asks for an JSON output and the data also to be used in a certain manner.

The entire structure of request that has to be sent to the openai API is designed in the completion section of the Program.

vulnerability analysis of {} and return a vulnerabilty report in json".format(analize) # A structure for the request completion = openai.Completion.create( engine=model_engine, prompt=prompt, max_tokens=1024, n=1, stop=None, ) response = completion.choices[0].text return response" dir="auto">
def profile(ip):
nm.scan('{}'.format(ip), arguments='-Pn -sS -sU -T4 -A -PE -PP -PS80,443 -PA3389 -PU40125 -PY -g 53 --script=vuln')
json_data = nm.analyse_nmap_xml_scan()
analize = json_data["scan"]
# Prompt about what the quary is all about
prompt = "do a vulnerability analysis of {} and return a vulnerabilty report in json".format(analize)
# A structure for the request
completion = openai.Completion.create(
engine=model_engine,
prompt=prompt,
max_tokens=1024,
n=1,
stop=None,
)
response = completion.choices[0].text
return response

Advantages

  • Can be used in developing a more advanced systems completly made of the API and scanner combination
  • Can increase the effectiveness of the final system
  • Highly productive when working with models such as GPT3


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