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Leverage the CISA Known Exploited Vulnerabilities catalog alongside EPSS and CVSS to prioritize CVE remediation based on real-world exploitation evidence.
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SKILL.md 표시 중
| name | performing-cve-prioritization-with-kev-catalog |
| description | Leverage the CISA Known Exploited Vulnerabilities catalog alongside EPSS and CVSS to prioritize CVE remediation based on real-world exploitation evidence. |
| domain | cybersecurity |
| subdomain | vulnerability-management |
| tags | ["cisa-kev","cve","vulnerability-prioritization","epss","bod-22-01","threat-intelligence","remediation"] |
| version | 1.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_ai_rmf | ["MEASURE-2.7","MAP-5.1","MANAGE-2.4"] |
| atlas_techniques | ["AML.T0070","AML.T0066","AML.T0082"] |
| nist_csf | ["ID.RA-01","ID.RA-02","ID.IM-02","ID.RA-06"] |
The CISA Known Exploited Vulnerabilities (KEV) catalog, established through Binding Operational Directive (BOD) 22-01, is a living list of CVEs that have been actively exploited in the wild and carry significant risk. As of early 2026, the catalog contains over 1,484 entries, growing 20% in 2025 alone with 245 new additions. This skill covers integrating the KEV catalog into vulnerability prioritization workflows alongside EPSS (Exploit Prediction Scoring System) and CVSS to create a risk-based approach that prioritizes vulnerabilities with confirmed exploitation activity over theoretical severity alone.
Each KEV entry contains:
| CVE Publication Date | Remediation Deadline |
|---|---|
| 2021 or later | 2 weeks from KEV listing |
| Before 2021 | 6 months from KEV listing |
| Factor | Weight | Data Source |
|---|
| Rationale |
|---|
| CISA KEV Listed | 30% | CISA KEV JSON feed | Confirmed active exploitation |
| EPSS Score | 25% | FIRST EPSS API | Predicted exploitation probability |
| CVSS Base Score | 20% | NVD API v2.0 | Intrinsic vulnerability severity |
| Asset Criticality | 15% | CMDB/Asset inventory | Business impact context |
| Network Exposure | 10% | Network architecture | Attack surface accessibility |
| KEV Listed | EPSS > 0.5 | CVSS >= 9.0 | Priority | SLA |
|---|---|---|---|---|
| Yes | Any | Any | P1-Emergency | 48 hours |
| No | Yes | Yes | P1-Emergency | 48 hours |
| No | Yes | No | P2-Critical | 7 days |
| No | No | Yes | P2-Critical | 7 days |
| No | No | No (>= 7.0) | P3-High | 14 days |
| No | No | No (>= 4.0) | P4-Medium | 30 days |
| No | No | No (< 4.0) | P5-Low | 90 days |
import requests
import json
from datetime import datetime
KEV_URL = "https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json"
def fetch_kev_catalog():
"""Download and parse the CISA KEV catalog."""
response = requests.get(KEV_URL, timeout=30)
response.raise_for_status()
data = response.json()
catalog = {}
for vuln in data.get("vulnerabilities", []):
cve_id = vuln["cveID"]
catalog[cve_id] = {
"vendor": vuln.get("vendorProject", ""),
"product": vuln.get("product", ""),
"name": vuln.get("vulnerabilityName", ""),
"date_added": vuln.get("dateAdded", ""),
"description": vuln.get("shortDescription", ""),
"action": vuln.get("requiredAction", ""),
"due_date": vuln.get("dueDate", ""),
"ransomware_use": vuln.get("knownRansomwareCampaignUse", "Unknown"),
}
print(f"[+] Loaded {len(catalog)} CVEs from CISA KEV catalog")
print(f" Catalog version: {data.get('catalogVersion', 'N/A')}")
print(f" Last updated: {data.get('dateReleased', 'N/A')}")
return catalog
kev = fetch_kev_catalog()
EPSS_API = "https://api.first.org/data/v1/epss"
def get_epss_scores(cve_list):
"""Fetch EPSS scores for a batch of CVEs."""
scores = {}
batch_size = 100
for i in range(0, len(cve_list), batch_size):
batch = cve_list[i:i + batch_size]
cve_param = ",".join(batch)
response = requests.get(EPSS_API, params={"cve": cve_param}, timeout=30)
if response.status_code == 200:
for entry in response.json().get("data", []):
scores[entry["cve"]] = {
"epss": float(entry.get("epss", 0)),
"percentile": float(entry.get("percentile", 0)),
}
return scores
import pandas as pd
def prioritize_vulnerabilities(scan_results, kev_catalog, epss_scores):
"""Apply multi-factor prioritization to scan results."""
prioritized = []
for vuln in scan_results:
cve_id = vuln.get("cve_id", "")
cvss_score = float(vuln.get("cvss_score", 0))
asset_criticality = float(vuln.get("asset_criticality", 3))
exposure = float(vuln.get("network_exposure", 3))
in_kev = cve_id in kev_catalog
kev_data = kev_catalog.get(cve_id, {})
epss_data = epss_scores.get(cve_id, {"epss": 0, "percentile": 0})
epss_score = epss_data["epss"]
# Composite risk score calculation
risk_score = (
(1.0 if in_kev else 0.0) * 10 * 0.30 +
epss_score * 10 * 0.25 +
cvss_score * 0.20 +
(asset_criticality / 5.0) * 10 * 0.15 +
(exposure / 5.0) * 10 * 0.10
)
# Assign priority level
if in_kev or (epss_score > 0.5 and cvss_score >= 9.0):
priority = "P1-Emergency"
sla_days = 2
elif epss_score > 0.5 or cvss_score >= 9.0:
priority = "P2-Critical"
sla_days = 7
elif cvss_score >= 7.0:
priority = "P3-High"
sla_days = 14
elif cvss_score >= 4.0:
priority = "P4-Medium"
sla_days = 30
else:
priority = "P5-Low"
sla_days = 90
prioritized.append({
"cve_id": cve_id,
"cvss_score": cvss_score,
"epss_score": round(epss_score, 4),
"epss_percentile": round(epss_data["percentile"], 4),
"in_cisa_kev": in_kev,
"ransomware_use": kev_data.get("ransomware_use", "N/A"),
"kev_due_date": kev_data.get("due_date", "N/A"),
"risk_score": round(risk_score, 2),
"priority": priority,
"sla_days": sla_days,
"asset": vuln.get("asset", ""),
"asset_criticality": asset_criticality,
})
df = pd.DataFrame(prioritized)
df = df.sort_values("risk_score", ascending=False)
return df
def generate_report(df, output_file="kev_prioritized_report.csv"):
"""Generate summary report from prioritized vulnerabilities."""
print("\n" + "=" * 70)
print("VULNERABILITY PRIORITIZATION REPORT - KEV + EPSS + CVSS")
print("=" * 70)
print(f"\nTotal vulnerabilities analyzed: {len(df)}")
print(f"KEV-listed vulnerabilities: {df['in_cisa_kev'].sum()}")
print(f"Ransomware-associated: {(df['ransomware_use'] == 'Known').sum()}")
print("\nPriority Distribution:")
print(df["priority"].value_counts().to_string())
print("\nTop 15 Highest Risk Vulnerabilities:")
top = df.head(15)[["cve_id", "cvss_score", "epss_score", "in_cisa_kev",
"risk_score", "priority"]]
print(top.to_string(index=False))
df.to_csv(output_file, index=False)
print(f"\n[+] Full report saved to: {output_file}")