Triaging Vulnerabilities with SSVC Framework

Overview

The Stakeholder-Specific Vulnerability Categorization (SSVC) framework, developed by Carnegie Mellon University's Software Engineering Institute (SEI) in collaboration with CISA, provides a structured decision-tree methodology for vulnerability prioritization. Unlike CVSS alone, SSVC accounts for exploitation status, technical impact, automatability, mission prevalence, and public well-being impact to produce one of four actionable outcomes: Track, Track*, Attend, or Act.

Prerequisites

Python 3.9+ with requests, pandas, and jinja2 libraries
Access to CISA KEV catalog API and EPSS API from FIRST
NVD API key (optional, for higher rate limits)
Vulnerability scan results from tools like OpenVAS, Nessus, or Qualys

SSVC Decision Points

1. Exploitation Status

Assess current exploitation activity:

None - No evidence of active exploitation
PoC - Proof-of-concept exists publicly
Active - Active exploitation observed in the wild (check CISA KEV)

# Check if a CVE is in CISA Known Exploited Vulnerabilities catalog
curl -s "https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json" | \
  python3 -c "import sys,json; data=json.load(sys.stdin); cves=[v['cveID'] for v in data['vulnerabilities']]; print('Active' if 'CVE-2024-3400' in cves else 'Check PoC/None')"

2. Technical Impact

Determine scope of compromise if exploited:

Partial - Limited to a subset of system functionality or data
Total - Full control of the affected system, complete data access

3. Automatability

Evaluate if exploitation can be automated at scale:

No - Requires manual, targeted exploitation per victim
Yes - Can be scripted or worm-like propagation is possible

4. Mission Prevalence

How widespread is the affected product in your environment:

Minimal - Limited deployment, non-critical systems
Support - Supports mission-critical functions indirectly
Essential - Directly enables core mission capabilities

5. Public Well-Being Impact

Potential consequences for physical safety and public welfare:

Minimal - Negligible impact on safety or public services
Material - Noticeable degradation of public services
Irreversible - Loss of life, major property damage, or critical infrastructure failure

SSVC Decision Outcomes

Outcome	Action Required	SLA
Track	Monitor, remediate in normal patch cycle	90 days
Track*	Monitor closely, prioritize in next patch window	60 days
Attend	Escalate to senior management, accelerate remediation	14 days
Act	Apply mitigations immediately, executive-level awareness	48 hours

Implementation Steps

Step 1: Ingest Vulnerability Data

import requests
import json

# Fetch CISA KEV catalog
kev_url = "https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json"
kev_data = requests.get(kev_url).json()
kev_cves = {v['cveID'] for v in kev_data['vulnerabilities']}

# Fetch EPSS scores for context
epss_url = "https://api.first.org/data/v1/epss"
epss_response = requests.get(epss_url, params={"cve": "CVE-2024-3400"}).json()

Step 2: Evaluate Each Decision Point

def evaluate_exploitation(cve_id, kev_set):
    """Determine exploitation status from CISA KEV and EPSS data."""
    if cve_id in kev_set:
        return "active"
    epss = requests.get(
        "https://api.first.org/data/v1/epss",
        params={"cve": cve_id}
    ).json()
    if epss.get("data"):
        score = float(epss["data"][0].get("epss", 0))
        if score > 0.5:
            return "poc"
    return "none"

def evaluate_technical_impact(cvss_vector):
    """Parse CVSS vector for scope and impact metrics."""
    if "S:C" in cvss_vector or "C:H/I:H/A:H" in cvss_vector:
        return "total"
    return "partial"

def evaluate_automatability(cvss_vector, cve_description):
    """Check if attack vector is network-based with low complexity."""
    if "AV:N" in cvss_vector and "AC:L" in cvss_vector and "UI:N" in cvss_vector:
        return "yes"
    return "no"

Step 3: Apply SSVC Decision Tree

def ssvc_decision(exploitation, tech_impact, automatability, mission_prevalence, public_wellbeing):
    """CISA SSVC decision tree implementation."""
    if exploitation == "active":
        if tech_impact == "total" or automatability == "yes":
            return "Act"
        if mission_prevalence in ("essential", "support"):
            return "Act"
        return "Attend"
    if exploitation == "poc":
        if automatability == "yes" and tech_impact == "total":
            return "Attend"
        if mission_prevalence == "essential":
            return "Attend"
        return "Track*"
    # exploitation == "none"
    if tech_impact == "total" and mission_prevalence == "essential":
        return "Track*"
    return "Track"

Step 4: Generate Triage Report

# Run the SSVC triage script against scan results
python3 scripts/process.py --input scan_results.csv --output ssvc_triage_report.json

# View summary
cat ssvc_triage_report.json | python3 -m json.tool | head -50

Integration with Vulnerability Scanners

Import from Nessus CSV

# Export Nessus scan as CSV, then process
python3 scripts/process.py \
  --input nessus_export.csv \
  --format nessus \
  --output ssvc_results.json

Import from OpenVAS

# Export OpenVAS results as XML
python3 scripts/process.py \
  --input openvas_report.xml \
  --format openvas \
  --output ssvc_results.json

Validation and Testing

# Test SSVC decision logic with known CVEs
python3 -c "
from scripts.process import ssvc_decision
# CVE-2024-3400 - Palo Alto PAN-OS command injection (KEV listed)
assert ssvc_decision('active', 'total', 'yes', 'essential', 'material') == 'Act'
# CVE-2024-21887 - Ivanti Connect Secure (PoC available)
assert ssvc_decision('poc', 'total', 'yes', 'support', 'minimal') == 'Attend'
print('All SSVC decision tests passed')
"

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