Canary Detections - Vulnerability Research

import os
import vulncheck_sdk
import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import calplot
from dotenv import load_dotenv
from datetime import datetime

load_dotenv()

DEFAULT_HOST = "https://api.vulncheck.com"
DEFAULT_API = DEFAULT_HOST + "/v3"
TOKEN = os.environ["VULNCHECK_API_TOKEN"]
YEAR = 2025

# Configure the VulnCheck API client
configuration = vulncheck_sdk.Configuration(host=DEFAULT_API)
configuration.api_key["Bearer"] = TOKEN

import sys
sys.path.insert(0, "..")
from utils import load_index

cve = []
vendor = []
product = []
ransomware = []
date_added = []
cisa_date_added = []
canary = []
first_reference = []
first_reference_date = []

for entry in load_index("vulncheck-kev"):
    cve.append(entry.cve[0])
    vendor.append(entry.vendor_project)
    product.append(entry.product)
    ransomware.append(entry.known_ransomware_campaign_use)
    date_added.append(entry.date_added[:10])
    canary.append(entry.reported_exploited_by_vulncheck_canaries)

    if entry.cisa_date_added is None:
        cisa_date_added.append("none")
    else:
        cisa_date_added.append(entry.cisa_date_added[:10])

    if entry.vulncheck_reported_exploitation:
        first_ref = min(
            entry.vulncheck_reported_exploitation,
            key=lambda x: datetime.fromisoformat(x.date_added.replace("Z", ""))
        )
        first_reference.append(first_ref.url)
        first_reference_date.append(first_ref.date_added[:10])
    else:
        first_reference.append("none")
        first_reference_date.append("none")

df_original = pd.DataFrame({
    'CVE': cve,
    'Vendor': vendor,
    'Product': product,
    'Ransomware': ransomware,
    'Date Added': date_added,
    'CISA Date Added': cisa_date_added,
    'Canary': canary,
    'First Reference': first_reference,
    'First Reference Date': first_reference_date
})

VulnCheck Canary Intelligence provides exploitation data from globally deployed vulnerable hosts revealing the first signs of vulnerability exploitation and tracking which CVEs are being targeted in the wild, and by whom.

VulnCheck Canary Exploitation Detection Metrics¶

df = df_original.copy()

# --- Clean up data types ---
df['Date Added'] = pd.to_datetime(df['Date Added'], errors='coerce')

df['CISA Date Added'] = df['CISA Date Added'].replace('none', pd.NaT)
df['CISA Date Added'] = pd.to_datetime(df['CISA Date Added'], format='%Y-%m-%d', errors='coerce')

# Ensure Canary is boolean
df['Canary'] = df['Canary'].astype(bool)

# --- Filter for Canary detections ---
df_canary = df[df['Canary'] == True]

# --- Metrics ---
# Total Canary detections
total_cves_canary = df_canary['CVE'].nunique()

# Ransomware-related CVEs
ransomware_related = df_canary[
    df_canary['Ransomware'].str.lower() == 'known'
]['CVE'].nunique()

# CISA KEV presence
cisa_present = df_canary[df_canary['CISA Date Added'].notnull()]['CVE'].nunique()

# VulnCheck KEV presence
vulncheck_present = df_canary[df_canary['Date Added'].notnull()]['CVE'].nunique()

# Not listed in CISA KEV
not_on_cisa = df_canary[df_canary['CISA Date Added'].isnull()]['CVE'].nunique()

# 🔥 VulnCheck First to Detect (Option A — uses only FIRST reference)
vulncheck_first_to_detect = df_canary[
    df_canary['First Reference'].str.contains(
        "api.vulncheck.com/v3/index/vulncheck-canaries",
        na=False
    )
]['CVE'].nunique()

# --- Assemble the metrics table ---
stats_rows = [
    ("Detected Exploitation", total_cves_canary),
    ("On VulnCheck KEV", vulncheck_present),
    ("On CISA KEV", cisa_present),
    ("Not on CISA KEV", not_on_cisa),
    ("VulnCheck First to Detect", vulncheck_first_to_detect),   # ⬅️ NEW
    ("Associated with Ransomware", ransomware_related),
]

stats_df = pd.DataFrame(stats_rows, columns=["VulnCheck Canary Metrics", "CVEs"])
stats_df["CVEs"] = stats_df["CVEs"].fillna(0).astype(int)

# --- Apply simple table styling ---
styled_stats_df = (
    stats_df.style
    .set_properties(**{'text-align': 'center'})
    .set_table_styles([{'selector': 'th', 'props': [('text-align', 'center')]}])
    .set_table_attributes('style="width:100%; border-collapse: collapse;"')
    .hide(axis="index")
)

styled_stats_df

Known Exploited Vulnerabilities Reported by VulnCheck Canaries (Source: VulnCheck KEV)¶

import plotly.express as px
import pandas as pd

df = df_original.copy()

# Ensure 'Canary' column is boolean
df['Canary'] = df['Canary'].astype(bool)

# Filter only entries where Canary == True
canary_df = df[df['Canary'] == True]

# Count occurrences by Vendor and Product for Canary = True
vendor_product_counts = canary_df.groupby(['Vendor', 'Product']).size().reset_index(name='Counts')

# Truncate vendor and product names for readability
vendor_product_counts['Vendor'] = vendor_product_counts['Vendor'].str.slice(0, 15)
vendor_product_counts['Product'] = vendor_product_counts['Product'].str.slice(0, 15)

# Create treemap
fig = px.treemap(
    vendor_product_counts,
    path=['Vendor', 'Product'],
    values='Counts',
    color='Counts',
    color_continuous_scale='Viridis'
)

# Customize layout for dark mode
fig.update_layout(
    title="Known Exploited Vulnerabilities Reported by VulnCheck Canaries (Source: VulnCheck KEV)",
    title_font=dict(size=20, color='white'),
    title_x=0.5,
    paper_bgcolor='black',
    plot_bgcolor='black',
    margin=dict(t=50, l=25, r=25, b=25),
    width=1800,
    height=1000
)

# Remove color scale and style labels
fig.update_coloraxes(showscale=False)
fig.update_traces(
    texttemplate='%{label}<br>%{value}',
    textfont_size=16,
    textfont_color='white'
)

fig.show()

Known Exploited Vulnerabilities Reported by VulnCheck Canaries Not Present on CISA KEV (Source: VulnCheck KEV)¶

# Copy original dataframe
df = df_original.copy()

# --- Clean up data types ---
df['Date Added'] = pd.to_datetime(df['Date Added'], errors='coerce')
df['CISA Date Added'] = df['CISA Date Added'].replace('none', pd.NaT)
df['CISA Date Added'] = pd.to_datetime(df['CISA Date Added'], format='%Y-%m-%d', errors='coerce')

# Ensure Canary is boolean
df['Canary'] = df['Canary'].astype(bool)

# --- Filter for Canary detections not on CISA KEV ---
df_canary_not_cisa = df[(df['Canary'] == True) & (df['CISA Date Added'].isnull())]

# --- Count occurrences by Vendor and Product ---
vendor_product_counts = (
    df_canary_not_cisa
    .groupby(['Vendor', 'Product'])
    .size()
    .reset_index(name='Counts')
)

# Truncate vendor and product names for readability
vendor_product_counts['Vendor'] = vendor_product_counts['Vendor'].str.slice(0, 15)
vendor_product_counts['Product'] = vendor_product_counts['Product'].str.slice(0, 15)

# --- Create Treemap ---
fig = px.treemap(
    vendor_product_counts,
    path=['Vendor', 'Product'],
    values='Counts',
    color='Counts',
    color_continuous_scale='Viridis'
)

# --- Customize layout for dark mode ---
fig.update_layout(
    title="VulnCheck Canaries — CVEs Not Present on CISA KEV (Source: VulnCheck KEV)",
    title_font=dict(size=20, color='white'),
    title_x=0.5,
    paper_bgcolor='black',
    plot_bgcolor='black',
    margin=dict(t=50, l=25, r=25, b=25),
    width=1800,
    height=1000
)

# --- Remove color scale and style labels ---
fig.update_coloraxes(showscale=False)
fig.update_traces(
    texttemplate='%{label}<br>%{value}',
    textfont_size=16,
    textfont_color='white'
)

fig.show()

Initial Configuration¶

Pull Data¶

VulnCheck Canary Exploitation Detection Metrics¶

Known Exploited Vulnerabilities Reported by VulnCheck Canaries (Source: VulnCheck KEV)¶

Known Exploited Vulnerabilities Reported by VulnCheck Canaries Not Present on CISA KEV (Source: VulnCheck KEV)¶