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