Reserved CVEs by Reference Count

import os
import sys
import pandas as pd
from datetime import datetime
from dotenv import load_dotenv

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

load_dotenv()

cve_data = []

def process_entry(entry):
    try:
        cve_id = entry.id
        published = entry.published
        published_date = (
            datetime.fromisoformat(published.replace("Z", "")).date()
            if published else None
        )
        references = entry.references or []
        ref_count = len(references)
        first_ref_url = references[0].url if references else None
        first_ref_date_raw = references[0].date_added if references else None
        first_ref_date = (
            datetime.fromisoformat(first_ref_date_raw.replace("Z", "")).date()
            if first_ref_date_raw else None
        )
        cve_data.append({
            "CVE": cve_id,
            "Published Date": published_date,
            "Reference Count": ref_count,
            "First Reference URL": first_ref_url,
            "First Reference Date": first_ref_date
        })
    except Exception as e:
        print(f"Error processing entry {entry.id}: {e}")

for entry in stream_index("vulncheck-nvd2"):
    process_entry(entry)

df = pd.DataFrame(cve_data)
display(df.head())

This list includes reserved CVEs that have not yet been published, sorted in descending order based on the number of references associated with each.

from IPython.display import display, HTML
import pandas as pd

# Step 1: Filter only on Published Date being 1970-01-01
df_filtered = df[df["Published Date"] == pd.to_datetime("1970-01-01").date()].copy()

# Step 2: Sort by highest reference count
df_sorted = df_filtered.sort_values("Reference Count", ascending=False).reset_index(drop=True)

# Step 3: Truncate to Top 50
top_50 = df_sorted.head(50).copy()

# Step 4: Replace any NaNs with "-"
columns_to_clean = ["CVE", "Published Date", "Reference Count", "First Reference URL", "First Reference Date"]
for col in columns_to_clean:
    top_50[col] = top_50[col].fillna("-")

# Step 5: Format and render HTML
title_html = f"""
<h2 style="margin-bottom: 5px;">Top 50 Reserved CVEs by Public Reference Count</h2>
<p><strong>Total Matching CVEs: {len(df_filtered)}</strong></p>
"""

table_html = top_50[[
    "CVE",
    "Reference Count",
    "First Reference URL",
    "First Reference Date"
]].to_html(index=False, escape=False)

scrollable_html = f"""
{title_html}
<div style="max-height: 500px; overflow-y: scroll; border: 1px solid #ccc; padding: 10px">
    {table_html}
</div>
"""

display(HTML(scrollable_html))

Reserved CVEs by Reference Count¶