Uses python3.10, Debian, python-Nmap, and flask framework to create an Nmap API that can do scans with a good speed online and is easy to deploy.
This is an implementation for our college PCL project which is still under development and constantly updating.
GET /api/p1/{auth_key}/{target}
GET /api/p2/{auth_key}/{target}
GET /api/p3/{auth_key}/{target}
GET /api/p4/{auth_key}/{target}
GET /api/p5/{auth_key}/{target}
| Parameter | Type | Description |
|---|---|---|
auth_key |
string |
Required. The API auth key gebe |
target |
string |
Required. The target Hostname and IP |
GET /api/p1/
GET /api/p2/
GET /api/p3/
GET /api/p4/
GET /api/p5/
GET /api/p6/
GET /api/p7/
GET /api/p8/
GET /api/p9/
GET /api/p10/
GET /api/p11/
GET /api/p12/
GET /api/p13/
| 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 -PY -g 53 --script=vuln |
p6 |
json |
Comprehensive Service Version Detection | -Pn -sV -p- -A |
p7 |
json |
Aggressive Scan with OS Detection | -Pn -sS -sV -O -T4 -A |
p8 |
json |
Script Scan for Common Vulnerabilities | -Pn -sC |
p9 |
json |
Intense Scan, All TCP Ports | -Pn -p 1-65535 -T4 -A -v |
p10 |
json |
UDP Scan | -Pn -sU -T4 |
p11 |
json |
Service and Version Detection for Top Ports | -Pn -sV --top-ports 100 |
p12 |
json |
Aggressive Scan with NSE Scripts for Vulnerabilities | -Pn -sS -sV -T4 --script=default,discovery,vuln |
p13 |
json |
Fast Scan for Common Ports | -Pn -F |
GET /register/<int:user_id>/<string:password>
| Parameter | Type | Description |
|---|---|---|
ID |
Int |
user ID |
Passwd |
String |
User Passwd |
Added GPT functionality with chunking module.
The methodology is based on how Langchain GPT embeddings operate. Basically the operation goes like this:
Data -> Chunks_generator ─┐ ┌─> AI_Loop -> Data_Extraction -> Return_Data
├─> Chunk1 ─┤
├─> Chunk2 ─┤
├─> Chunk3 ─┤
└─> Chunk N ─┘
AI code:
def AI(analize: str) -> dict[str, any]: prompt = f""" Do a NMAP scan analysis on the provided NMAP scan information The NMAP output must return in a JSON format accorging to the provided output format. The data must be accurate in regards towards a pentest report. The data must follow the following rules: 1) The NMAP scans must be done from a pentester point of view 2) The final output must be minimal according to the format given. 3) The final output must be kept to a minimal. 4) If a value not found in the scan just mention an empty string. 5) Analyze everything even the smallest of data. 6) Completely analyze the data provided and give a confirm answer using the output format. The output format: {{ "critical score": [""], "os information": [""], "open ports": [""], "open services": [""], "vulnerable service": [""], "found cve": [""] }} NMAP Data to be analyzed: {analize} """ messages = [{"content": prompt, "role": "assistant"}] response = openai.ChatCompletion.create( model=model_engine, messages=messages, max_tokens=2500, n=1, stop=None, ) response = response['choices'][0]['message']['content'] ai_output = { "markdown": response } return ai_output
Default_Key: e43d4
newer updates are still in progress