external/anthropic-cybersecurity-skills/skills/triaging-vulnerabilities-with-ssvc-framework/SKILL.md
Triage and prioritize vulnerabilities using CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree framework to produce actionable remediation priorities.
npx skillsauth add seikaikyo/dash-skills triaging-vulnerabilities-with-ssvc-frameworkInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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.
requests, pandas, and jinja2 librariesAssess current exploitation activity:
# 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')"
Determine scope of compromise if exploited:
Evaluate if exploitation can be automated at scale:
How widespread is the affected product in your environment:
Potential consequences for physical safety and public welfare:
| 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 |
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()
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"
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"
# 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
# Export Nessus scan as CSV, then process
python3 scripts/process.py \
--input nessus_export.csv \
--format nessus \
--output ssvc_results.json
# Export OpenVAS results as XML
python3 scripts/process.py \
--input openvas_report.xml \
--format openvas \
--output ssvc_results.json
# 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')
"
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.