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class Node(BaseModel):
    """
    Node class for the knowledge graph. Each node represents an entity.
    
    Attributes:
        id (int): Unique identifier for the node.
        label (str): Label or name of the node.
        color (str): Color of the node.
        num_targets (int): Number of target nodes connected to this node.

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Incident Report: Microsoft Storm-0558 Exploit and Attack Analysis

Incident Overview and Summary

This notification is to inform you about a recent security incident that occurred within our organization. We take the security and privacy of your data seriously, and we want to provide you with a detailed overview of the incident.

Incident Description

Microsoft has published details of how a China-based threat actor known as Storm-0558 was able to exploit security gaps and acquire a Microsoft account (MSA) consumer signing key. This key was then used to forge tokens and gain unauthorized access to enterprise email accounts of multiple Microsoft customers. Storm-0558 is a cyber espionage group that has been active since at least 2021 and has targeted various organizations, including US and European diplomatic entities, legislative governing bodies, media companies, internet service providers, and telecommunications equipment manufacturers. The group has employed various tactics such as credential ha

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Incident Report Template

<incident_report_template>

        ## Incident Overview and Summary

        ## Incident Description

        This notification is to inform you about a recent security incident that occurred within our organization. We take the security and privacy of your data seriously, and we want to provide you with a detailed overview of the incident.

Incident Timeline

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import os
os.environ["HUGGINGFACE_TOKEN"] = "hf_xxx"

def download_model_to_folder():
    from huggingface_hub import snapshot_download

    snapshot_download(
        "meta-llama/Llama-2-13b-chat-hf",
        local_dir="/model",
        token=os.environ["HUGGINGFACE_TOKEN"],

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import streamlit as st
import numpy as np
from random import randrange
import openai,boto3,urllib, requests
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
from PIL import Image
import re,json

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for i in $(aws ec2 describe-regions --region us-east-1  | grep RegionName | awk '{ print $2}' | sed -e 's/"//g') ; do aws ec2 describe-instances --region $i  | jq -r  ".Reservations[] | .Instances[] |  .Tags[] | select(.Key==\"Name\") |.Value " ; done | grep -i test

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curl -s -H "Content-Type: application/json"  -X POST https://phqk9h8w60.execute-api.us-east-1.amazonaws.com/prod --data '{"mystate":"your_state","mycounty":"your_county", "store":"grocery_or_supermarket"}' | xargs printf '%s\n'

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def take_cve_parse_nvd_give_cvssv3(cve):
    time.sleep(0.5)
    base_url = 'https://nvd.nist.gov/vuln/detail/'
    headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.88 Safari/537.36', 'referer':'www.google.com' }
    url = base_url + cve
    my_session = requests.session()
    for_cookies = my_session.get(url)
    cookies = for_cookies.cookies
    print(url)

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def parse_debian_tracker_give_json(cve):
    url = 'https://security-tracker.debian.org/tracker/' + cve
    page = requests.get(url)
    soup = BeautifulSoup(page.text, 'html.parser')
    df_arr = []
    for table in soup.find_all('table'):
        df = scrape_helpers.parse_html_table(table)
        df = df.iloc[0:1].reset_index()
        df=df.drop(['index'], axis='columns')
        if 'Debian Bugs' in df.columns:

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