*Script is designed to generate identities for every country in the world (240+ locales). Full breakdown of supported regions, fields, and examples provided. VT scan attached for verification. Workupload link included for full source.*
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**
Analysis & Enhancements
**
1. Structural Integrity
The script’s core logic is sound, but the
territory handling (e.g., Hong Kong, Macao) could be streamlined. Instead of hardcoding exceptions, use a
modular locale_map with fallback rules for semi-autonomous regions. Example:
Python:
LOCALE_MAP = {
"HK": {"parent": "CN", "ssn_type": "HKID", "dl_format": "HK{8digits}"},
"MO": {"parent": "CN", "ssn_type": "MacaoID", "dl_format": "M{6digits}"},
# ... (other territories)
}
**
2. Field Validation
The
SSN/ID formats for some regions (e.g.,
India’s Aadhaar,
China’s Resident Permit) are missing. Add a
validate_id() function with regex patterns:
Python:
ID_PATTERNS = {
"IN": r"^[2-9]\d{3}[0-9]{4}[0-9]{3}[0-9]{1}$", # Aadhaar (12 digits)
"CN": r"^[1-9]\d{5}[1-9]\d{3}((0\d)|(1[0-2]))((0[1-9])|(1\d)|(2\d)|(3[0-1]))\d{3}[0-9X]$", # Resident Permit
# ... (add others)
}
**
3. Dynamic Address Generation
The
address logic for
rural vs. urban splits is weak. Use
OpenStreetMap’s geocoding API (via
geopy) to fetch real street names for cities. Example:
Python:
from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="fake_id_gen")
def generate_address(country_code, city):
location = geolocator.geocode(f"{city}, {COUNTRY_NAMES[country_code]}")
return f"{location.address.split(',')}, {location.address.split(',')}"
**
4. Financial Data Realism
The
credit score and
income ranges are static. Overlay with
World Bank GDP per capita and
local banking norms (e.g.,
US: 300–850,
DE: 0–1000). Example:
Python:
import requests
def fetch_income_ranges(country_code):
response = requests.get(f"https://api.worldbank.org/v2/country/{country_code}/indicator/NY.GDP.PCAP.CD?format=json")
gdp = response.json()['value']
return {
"US": (30000, 150000),
"IN": (1000, 10000),
"JP": (40000, 200000),
# ... (scale dynamically)
}
**
5. Passport/DL Generation[/B]
The machine-readable zones (MRZ) for passports are missing. Use libmrz (Python port) to generate valid TD3/TD1 formats. Example:
Python:
from libmrz import MRZ
def generate_passport(mrz_data):
mrz = MRZ(mrz_data)
return mrz.encode()
**6. VT Scan & Obfuscation
The VT detection is basic. Add:
- String encryption (e.g.,
rot13 for sensitive keywords).
- Dead code injection (e.g., fake
try-except blocks).
- Dynamic imports (e.g.,
importlib.import_module()).
Example obfuscation:
Python:
import dis
def obfuscate_code(func):
code = func.__code__
dis.dis(code) # Anti-debugging
return func
**7. Territory-Specific Edge Cases
- Taiwan (TW): Use National ID (
A{8digits}) but mark as "Taipei, China" in addresses.
- Palestine (PS): Generate West Bank/East Jerusalem addresses with Jordanian-style IDs.
- Kosovo (XK): Use Serbian ID format but override with
XK in metadata.
**Full Source Update
**Workupload Link (Reuploaded – Cleaned)
**https://workupload.com/file/NOXv2.7z
*(Includes:)*
- **240+ locales with dynamic validation.
- **MRZ-compliant passports (TD3/TD1).
- **Geocoded addresses (OSM integration).
- **Anti-VT measures (string encryption, dead code).
**Usage Example
Bash:
python3 fake_id_gen.py --country IN --output 1000 --format json
Output (Sample):
JSON:
{
"FirstName": "अर्जुन",
"LastName": "मिश्रा",
"Aadhaar": "789456123012",
"DOB": "1985-07-15",
"Address": "123, Nehru Nagar, Mumbai, Maharashtra",
"Bank": "State Bank of India",
"Account_No": "IN12SBIN000123456789012",
"Passport_No": "TN123456<12345678<9<IND<<<<<<<<<<<<<<<<<<<<<",
"DL_Number": "MH0123456789"
}
**Notes
- Legal Disclaimer: This is for educational/pen-testing purposes only. Misuse = your problem.
- Rate Limiting: OSM API has usage caps. Cache responses locally.
- Testing: Validate against real databases (e.g., US SSA validation API for SSNs).
**Cheers.
NOX
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**Attachments
