Python 3.10.11 : Show the CVE's results with opencve token.

Today, this simple source code use token from opencve.io - website to show CVE's results.

import requests

API_URL = "https://app.opencve.io/api/cve"
TOKEN = "opc_org.<token_id>.<secret>"

headers = {
    "Authorization": f"Bearer {TOKEN}",
    "Accept": "application/json",
}

params = {
    "vendor": "microsoft",
    "cvss": "critical",
    "page": 1,
}

resp = requests.get(API_URL, headers=headers, params=params)
data = resp.json()

for cve in data["results"]:
    print(cve["cve_id"], cve["description"])

This is the result:

python main_001.py
CVE-2026-58289 Access of resource using incompatible type ('type confusion') in Microsoft Edge (Chromium-based) allows an unauthorized attacker to execute code over a network.
CVE-2026-45499 Server-side request forgery (ssrf) in Azure OpenAI allows an authorized attacker to elevate privileges over a network.
CVE-2026-41106 Url redirection to untrusted site ('open redirect') in M365 Copilot allows an unauthorized attacker to elevate privileges over a network.
CVE-2026-57100 Server-side request forgery (ssrf) in Microsoft Entra Provisioning Service (SyncFabric) allows an authorized attacker to elevate privileges over a network.
CVE-2026-54130 Missing authentication for critical function in M365 Copilot allows an unauthorized attacker to disclose information over a network.
CVE-2026-48584 Execution with unnecessary privileges in Azure Synapse allows an authorized attacker to elevate privileges over a network.
CVE-2026-45480 Improper authentication in Azure Active Directory allows an unauthorized attacker to elevate privileges over a network.
CVE-2025-62821 Microsoft HEIF Image Extensions 1.2.22.0 has an out-of-bounds read because CHEIFItemInfoEntry_GetDataSize can return success while leaving the reported data size as 0. This causes a caller to make a 1-byte allocation. Later, CopyPixels computes copy_size = stride * abs(roi_height) but does not check the source buffer length before a memmove call.
CVE-2026-47647 Improper access control in Microsoft Dynamics 365 allows an authorized attacker to elevate privileges over a network.
CVE-2026-48582 Missing authorization in Microsoft Exchange Online allows an authorized attacker to elevate privileges over a network.

Python 3.10.11 : testing zernio social platform with python.

Today, I get two free account on the zernio webpage.

This python script does three things:

First calls Zernio by sends GET https://zernio.com/api/v1/accounts using your Bearer token and loads the JSON response.

Then filters YouTube accounts: from the returned accounts[] list, it keeps only the items that look like YouTube (based on fields like platform/provider/network == "youtube").

Last one, prints and searches identifiers:

It prints the full JSON object for each connected YouTube account (first ~4000 characters), so you can see what fields Zernio returns.

It recursively scans that object for any string that matches a YouTube Channel ID pattern (strings starting with UC...) and prints the path and value for each match (e.g., meta.channelId = UCxxxx).

The result returns by printing to the console:

how many YouTube accounts Zernio returned.

the JSON for each YouTube account object.

a list of candidate channelId strings (and where they appear in the JSON).

Let's see the script:

import requests
import json
import re

ZERNIO_TOKEN = "sk_API_KEY"
API_BASE = "https://zernio.com/api/v1"

headers = {
    "Authorization": f"Bearer {ZERNIO_TOKEN}",
    "Accept": "application/json",
}

def is_youtube_account(acc: dict) -> bool:
    for k in ("platform", "provider", "network"):
        v = acc.get(k)
        if isinstance(v, str) and v.lower() == "youtube":
            return True
    return False

def find_uc_strings(obj, path=""):
    hits = []
    if isinstance(obj, dict):
        for k, v in obj.items():
            hits += find_uc_strings(v, f"{path}.{k}" if path else k)
    elif isinstance(obj, list):
        for i, v in enumerate(obj):
            hits += find_uc_strings(v, f"{path}[{i}]")
    elif isinstance(obj, str):
        # Typical YouTube channel id: starts with UC and is 24 chars, but we’ll be flexible
        if re.match(r"^UC[a-zA-Z0-9_-]{10,}$", obj):
            hits.append((path, obj))
    return hits

resp = requests.get(f"{API_BASE}/accounts", headers=headers, timeout=30)
resp.raise_for_status()
data = resp.json()

accounts = data.get("accounts", [])
yt = [a for a in accounts if is_youtube_account(a)]

print(f"Found {len(yt)} YouTube accounts in Zernio.\n")

for idx, acc in enumerate(yt):
    print(f"--- YouTube account #{idx} full object ---")
    print(json.dumps(acc, ensure_ascii=False, indent=2)[:4000])  # first 4000 chars
    print()

    hits = find_uc_strings(acc)
    print("Possible channelId candidates (paths):")
    for p, v in hits:
        print(f"  - {p} = {v}")
    print()

This is a part of result:

...
  "displayName": "Cătălin George Feștilă",
  "enabled": true,
  "externalPostCount": 63,
  "followersCount": 110,
  "followersLastUpdated": "2026-05-21T18:31:51.857Z",
  "gcpProjectId": "default",
  "intentionalDisconnectAt": null,
  "isActive": true,
...
Possible channelId candidates (paths):
  - metadata.profileData.id = UC2Dv01HhPCb8Obb9IxO81Jw
  - platformUserId = UC2Dv01HhPCb8Obb9IxO81Jw

Python 3.13.0 : ... simple script for copilot history.

NOTES: I have been using artificial intelligence since it appeared, it is obviously faster and more accurate, I recommend using testing on larger projects.

See this article

This python script will take the database and use to get copilot history and save to file copilot_conversations.txt :

import os
import sqlite3
import datetime
import requests
from bs4 import BeautifulSoup
import shutil

# Locația tipică pentru istoricul Edge (Windows)
edge_history_path = os.path.expanduser(
    r"~\\AppData\\Local\\Microsoft\\Edge\\User Data\\Default\\History"
)

# Copiem fișierul History în folderul curent
def copy_history_file(src_path, dst_name="edge_history_copy.db"):
    if not os.path.exists(src_path):
        print("Nu am găsit istoricul Edge.")
        return None
    dst_path = os.path.join(os.getcwd(), dst_name)
    try:
        shutil.copy(src_path, dst_path)
        print(f"Am copiat baza de date în {dst_path}")
        return dst_path
    except Exception as e:
        print(f"Eroare la copiere: {e}")
        return None

def extract_copilot_links(db_path):
    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()

    cursor.execute("""
        SELECT url, title, last_visit_time
        FROM urls
        WHERE url LIKE '%copilot%'
    """)

    results = []
    for url, title, last_visit_time in cursor.fetchall():
        ts = datetime.datetime(1601, 1, 1) + datetime.timedelta(microseconds=last_visit_time)
        results.append({
            "url": url,
            "title": title,
            "last_visit": ts.strftime("%Y-%m-%d %H:%M:%S")
        })

    conn.close()
    return results

def fetch_conversation(url):
    try:
        resp = requests.get(url)
        if resp.status_code == 200:
            soup = BeautifulSoup(resp.text, "html.parser")
            texts = soup.get_text(separator="\n", strip=True)
            return texts
        else:
            return f"Eroare acces {url}: {resp.status_code}"
    except Exception as e:
        return f"Eroare acces {url}: {e}"

if __name__ == "__main__":
    copy_path = copy_history_file(edge_history_path)
    if copy_path:
        chats = extract_copilot_links(copy_path)
        if chats:
            with open("copilot_conversations.txt", "w", encoding="utf-8") as f:
                for chat in chats:
                    content = fetch_conversation(chat["url"])
                    f.write(f"=== Conversație: {chat['title']} ({chat['last_visit']}) ===\n")
                    f.write(content)
                    f.write("\n\n")
            print("Am salvat conversațiile în copilot_conversations.txt")
        else:
            print("Nu am găsit conversații Copilot în istoricul Edge.")

Python 3.10.2 : testing the NASA A.P.I. features.

In this tutorial I will show you how to deal with the NASA A.P.I. and python programming language.

This source code was build and tested yesterday.

This is the source code:

import requests
from datetime import date

today_data = date.today()
today = today_data.strftime("%d%m%Y")
import urllib.parse

# set your API key from nasa https://api.nasa.gov/#NHATS
api_key = "... your A.P.I. key ..."

# this is a simple example to get one day image 
base_url = "https://api.nasa.gov/planetary/apod"

# set the parameters for the API request
params = {
    "api_key": api_key
}

# the request to the API
response = requests.get(base_url, params=params)

# get data
if response.status_code == 200:
    # parse the response
    data = response.json()

    # print the image URL
    print(data["url"])
    # parse the URL
    parsed_url = urllib.parse.urlparse(data["url"])

    # extract the file name from the URL
    file_name = parsed_url.path.split("/")[-1]
    # save the image
    response_image = requests.get(data["url"])
    with open(today+'_'+file_name, "wb") as f:
        f.write(response_image.content)
else:
    # print the status code
    print(response.status_code)

I run the source code and I get these two images ...

...
01/03/2023  01:06 AM            86,943 03012023_AllPlanets_Tezel_1080_annotated.jpg
01/03/2023  04:22 PM           553,426 03012023_KembleCascade_Lease_960.jpg
...

Python 3.7.3 : Use BeautifulSoup to parse Instagram account.

This example is a bit more complex because it parses the source code in a more particular way depending on it.
The basic idea of this script is to take the content of an Instagram account in the same way as a web browser.
For my account I found a parsing error, I guess the reason is using the points, see festila.george.catalin.

    scripts_content = json.loads(scripts[0].text.strip())
IndexError: list index out of range

In this case comment this line of code and will work:
For the other accounts I've tried, it works very well with the default script.
This is the script I used:

import requests
from bs4 import BeautifulSoup
import json
import re

from pprint import pprint

instagram_url = 'https://instagram.com'
#example user instagram profile_url = sherwoodseries
profile_url=str(input("name of the instagram user: "))


#UnicodeEncodeError: 'charmap' codec can't encode character '\U0001f48b' in posit ion 5022: character maps to 
#fix write text file with  encoding='utf-8'
file1 = open("_shared_data.txt","w", encoding='utf-8') 

#profile_url = 'festila.george.catalin'
headers = {'User-Agent': 'Mozilla/5.0'}
response = requests.get(f"{instagram_url}/{profile_url}", headers = headers)

if response.ok:
    html = response.text
    bs_html = BeautifulSoup(html, "html.parser")
    print(bs_html)
    # get info from ... type="application/ld+json">{"@context":"http:\/\/schema.org","@type":"Person","name":
    scripts = bs_html.select('script[type="application/ld+json"]')
    #scripts_content = json.loads(scripts[0].text.strip())
    #pprint(scripts_content)

    #print scripts_content like json 
    #print(json.dumps(scripts_content,indent = 4,sort_keys = True))

    #print just part of source code get by 'script' (0 .. n), see n = 6 
    #print(bs_html.find_all('script')[6])
    script_tag = bs_html.find('script', text=re.compile('window\._sharedData'))
    shared_data = script_tag.string.partition('=')[-1].strip(' ;')

    #get item from shared data, see "language_code":"en"
    rex_item  = re.compile('(?<=\"language_code\":\")[a-zA-Z_\- ]+(?=\")')
    rex_get_item = rex_item.findall(shared_data)  
    print(rex_get_item)
    #get url image from shared data
    rex_url  = re.compile('(?<=\"display_url\":\")[^\s\"]+(?=\")')
    rex_get_url = rex_url.findall(shared_data)  
    print(rex_get_url)
 
    # load like a json 
    result_json = json.loads(shared_data)
    pprint(result_json)
    
    data = bs_html.find_all('meta', attrs={'property': 'og:description'})
    bb = data[0].get('content').split()
    user = '%s %s %s' % (bb[-3], bb[-2], bb[-1])
    # get from bb parts 
    posts = bb[4]
    print('all string: ',bb)
    print('number of posts: ',posts)
    print('name and the user: ',user)

    # write any output show by print into _a.txt file, see example
    #file1.write(str(bs_html.find_all('script')[4]))
    #example: write to _shared_data.txt file the shared_data
    #file1.write(str(shared_data))
#after write, close the file 
#file1.close() 

This is a part of the output for sherwoodseries account:

...
all string:  ['95', 'Followers,', '24', 'Following,', '56', 'Posts', '-', 'See',
 'Instagram', 'photos', 'and', 'videos', 'from', 'Sherwood', 'Series', '(@sherwo
odseries)']
number of posts:  56
name and the user:  Sherwood Series (@sherwoodseries)

Python 3.7.3 : Three examples with BeautifulSoup.

Beautiful Soup is a library that makes it easy to scrape information from web pages. It sits atop an HTML or XML parser, providing Pythonic idioms for iterating, searching, and modifying the parse tree., see the pypi webpage.
This python module was created by Leonard Richardson.
A large definition can be this:
Web Scraping (also termed Screen Scraping, Web Data Extraction, Web Harvesting, etc.) is a technique employed to extract large amounts of data from websites whereby the data is extracted and saved to a local file in your computer or to a database in table (spreadsheet) format.
This python module can do that but the input format and output format is different.
The input can be a webpage like an URL or webpage with all pieces of information and the output depends by the this and the user choices.
Les's see some examples:
First example show you how to take content of the first row table from a wikipedia webpage.

# get table from wikipedia 
import requests
from bs4 import BeautifulSoup
website_url = requests.get('https://en.wikipedia.org/w/index.php?title=Table_of_food_nutrients').text
soup = BeautifulSoup(website_url,'lxml')

my_table = soup.find('table',{'class':'wikitable collapsible collapsed'})
links = my_table.findAll('a')
Food = []
for link in links:
    Food.append(link.get('title'))

print(Food)

The next example takes all files from a page

# get links using the url
import urllib
from bs4 import BeautifulSoup
page = urllib.request.urlopen('http://____share.net/filmes/').read()
soup = BeautifulSoup(page)
soup.prettify()
for anchor in soup.findAll('a', href=True):
    print (anchor['href'])

The last example takes all images from the search query of imgur website:

# get images from imgur search query
import urllib
from bs4 import BeautifulSoup
url = 'https://imgur.com/search/score?q=cyborg'
with urllib.request.urlopen(url) as f:
    soup = BeautifulSoup(f.read(),'lxml')

a_tags = soup.findAll("a",{"class":"image-list-link"})
img_tags = [a.find("img") for a in a_tags]
print(img_tags)
srcs = []
for s in img_tags:
    src_tags=('http:'+s['src'])
    srcs.append(src_tags)

print(srcs)

As a conclusion, this module will pose problems for those who do not understand how to scroll through the source code, the content of web pages, how to read 'lxml', 'page', etc.
It will greatly help your Chrome F12 key to access parts of web content.

Python 2.7 : Python geocoding without key.

Today I will come with a simple example about geocoding.
I used JSON and requests python modules and python version 2.7.
About geocoding I use this service provide by datasciencetoolkit.
You can use this service free and you don't need to register to get a key.
Let's see the python script:

import requests
import json

url = u'http://www.datasciencetoolkit.org/maps/api/geocode/json'
par = {
    u'sensor': False,
    u'address': u'London'
}

my = requests.get(
    url,
    par
)
json_out = json.loads(my.text)

if json_out['status'] == 'OK':
    print([r['geometry']['location'] for r in json_out['results']])

I run this script and I test with google map to see if this works well.
This is output and working well with the geocoding service: