GTP Vulnerability Analyzer -

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

Requirements for GTP Vulnerability Analyzer

Python 3.10
All the packages mentioned in the requirements.txt file
Open AI API

Usage

First Change the “API__KEY” part of the code with Open AI 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 crucial arguments for the scan to continue. neatly 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 labeled according to the ports opened. once the data is extracted the data is sent to Open AI API DaVinci 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 Open AI API is designed in the completion section of the Program.

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 quarry is all about
    prompt = "do a vulnerability analysis of {} and return a vulnerability report in JSON".format(analyze)
    # 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 completely made of the API and scanner combination
Can increase the effectiveness of the final system
Highly productive when working with models such as GPT3