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performing-cve-prioritization-with-kev-catalog 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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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"]
Performing CVE Prioritization with KEV Catalog
Overview
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.
When to Use
When conducting security assessments that involve performing cve prioritization with kev catalog
When following incident response procedures for related security events
When performing scheduled security testing or auditing activities
When validating security controls through hands-on testing
Prerequisites
Access to vulnerability scan results (Qualys, Nessus, Rapid7, etc.)
Familiarity with CVE identifiers and NVD
Understanding of CVSS scoring (v3.1 and v4.0)
API access to CISA KEV, EPSS, and NVD endpoints
Python 3.8+ with requests and pandas libraries
Core Concepts
CISA KEV Catalog Structure
Each KEV entry contains:
CVE ID : The CVE identifier (e.g., CVE-2024-3094)Vendor/Project : Affected vendor and product nameVulnerability Name : Short description of the vulnerabilityDate Added : When CISA added it to the catalogShort Description : Brief technical descriptionRequired Action : Recommended remediation actionDue Date : Deadline for federal agencies (FCEB) to remediateKnown Ransomware Campaign Use : Whether ransomware groups exploit it
BOD 22-01 Remediation Timelines
CVE Publication Date Remediation Deadline 2021 or later 2 weeks from KEV listing Before 2021 6 months from KEV listing
Multi-Factor Prioritization Model
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 + EPSS Decision Matrix 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
Workflow
Step 1: Fetch and Parse the KEV Catalog 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()
Step 2: Enrich with EPSS Scores 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
Step 3: Build the Prioritization Engine 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" ]
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
)
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
Step 4: Generate Prioritization Report 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} " )
Best Practices
Update the KEV catalog daily since CISA adds new entries multiple times per week
Always cross-reference KEV with EPSS; a CVE may have high EPSS but not yet be in KEV
Treat all KEV-listed CVEs as P1-Emergency regardless of CVSS score
Pay special attention to KEV entries flagged with "Known Ransomware Campaign Use"
Automate KEV comparison against your vulnerability scan results in CI/CD pipelines
Track KEV due dates separately for FCEB compliance requirements
Use KEV as a leading indicator for threat hunting; if a CVE is added, check for prior exploitation in your environment
Common Pitfalls
Relying solely on CVSS scores without checking KEV or EPSS data
Not updating the KEV catalog frequently enough (CISA updates multiple times weekly)
Treating non-KEV CVEs as safe; they may be exploited but not yet cataloged
Ignoring the "ransomware use" field which indicates highest-urgency threats
Using KEV only for compliance instead of integrating into overall risk management
Related Skills
prioritizing-vulnerabilities-with-cvss-scoring
building-vulnerability-data-pipeline-with-api
implementing-threat-intelligence-scoring
implementing-vulnerability-remediation-sla
