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.
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
