implementing-epss-score-for-vulnerability-prioritization

Integrate FIRST's Exploit Prediction Scoring System (EPSS) API to prioritize vulnerability remediation based on real-world exploitation probability within 30 days. Use when integrateing first's exploit prediction scoring system (epss) api to prioritize.

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name	implementing-epss-score-for-vulnerability-prioritization
description	Integrate FIRST's Exploit Prediction Scoring System (EPSS) API to prioritize vulnerability remediation based on real-world exploitation probability within 30 days. Use when integrateing first's exploit prediction scoring system (epss) api to prioritize.
domain	cybersecurity
subdomain	vulnerability-management
tags	["epss","vulnerability-prioritization","first","exploit-prediction","cvss","risk-based","machine-learning"]
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["ID.RA-01","ID.RA-02","ID.IM-02","ID.RA-06"]

Implementing EPSS Score for Vulnerability Prioritization

Overview

The Exploit Prediction Scoring System (EPSS) is a data-driven model developed by FIRST (Forum of Incident Response and Security Teams) that estimates the probability of a CVE being exploited in the wild within the next 30 days. EPSS produces scores from 0.0 to 1.0 (0% to 100%) using machine learning trained on real-world exploitation data. Unlike CVSS which measures severity, EPSS measures likelihood of exploitation, making it essential for risk-based vulnerability prioritization.

When to Use

Trigger phrases:

"implementing epss score for vulnerability prioritization"
"Integrate FIRST's Exploit Prediction Scoring System (EPSS) API to prioritize vul"
When deploying or configuring implementing epss score for vulnerability prioritization capabilities in your environment
When establishing security controls aligned to compliance requirements
When building or improving security architecture for this domain
When conducting security assessments that require this implementation

Prerequisites

Python 3.9+ with requests, pandas, matplotlib
Access to FIRST EPSS API (https://api.first.org/data/v1/epss)
Vulnerability scan results with CVE identifiers
Optional: NVD API key for CVSS enrichment

EPSS API Usage

# Basic usage example
# Replace with domain-specific commands from the workflow above

Query Single CVE

# Get EPSS score for a specific CVE
curl -s "https://api.first.org/data/v1/epss?cve=CVE-2024-3400" | python3 -m json.tool

# Response:
# {
#   "status": "OK",
#   "status-code": 200,
#   "version": "1.0",
#   "total": 1,
#   "data": [
#     {
#       "cve": "CVE-2024-3400",
#       "epss": "0.95732",
#       "percentile": "0.99721",

# Batch query up to 100 CVEs
curl -s "https://api.first.org/data/v1/epss?cve=CVE-2024-3400,CVE-2024-21887,CVE-2023-44228" | \
  python3 -c "
import sys, json
data = json.load(sys.stdin)
for item in data['data']:
    pct = float(item['epss']) * 100
    print(f\"{item['cve']}: {pct:.2f}% exploitation probability (percentile: {item['percentile']})\")
"

# Download complete daily EPSS scores (CSV format)
curl -s "https://epss.cyentia.com/epss_scores-current.csv.gz" | gunzip > epss_scores_current.csv

# Check size and preview
wc -l epss_scores_current.csv
head -5 epss_scores_current.csv

# Get EPSS score for a specific date
curl -s "https://api.first.org/data/v1/epss?cve=CVE-2024-3400&date=2024-04-12"

# Get time series data
curl -s "https://api.first.org/data/v1/epss?cve=CVE-2024-3400&scope=time-series"

EPSS Score	CVSS Score	Priority	Action
> 0.7	>= 9.0	P0 - Immediate	Remediate within 24 hours
> 0.7	>= 7.0	P1 - Urgent	Remediate within 48 hours
> 0.4	>= 7.0	P2 - High	Remediate within 7 days
> 0.1	>= 4.0	P3 - Medium	Remediate within 30 days
<= 0.1	>= 7.0	P3 - Medium	Remediate within 30 days
<= 0.1	< 7.0	P4 - Low	Remediate within 90 days

import requests
import pandas as pd
from datetime import datetime

def fetch_epss_scores(cve_list):
    """Fetch EPSS scores for a list of CVEs from FIRST API."""
    scores = {}
    batch_size = 100
    for i in range(0, len(cve_list), batch_size):
        batch = cve_list[i:i + batch_size]
        resp = requests.get(
            "https://api.first.org/data/v1/epss",
            params={"cve": ",".join(batch)},
            timeout=30
        )
        if resp.status_code == 200:
            for entry in resp.json().get("data", []):
                scores[entry["cve"]] = {
                    "epss": float(entry["epss"]),
                    "percentile": float(entry["percentile"]),
                    "date": entry.get("date", ""),
                }
    return scores

def prioritize_vulnerabilities(scan_results_csv, output_csv):
    """Enrich scan results with EPSS scores and assign priorities."""
    df = pd.read_csv(scan_results_csv)
    cve_list = df["cve_id"].dropna().unique().tolist()

    epss_data = fetch_epss_scores(cve_list)

    df["epss_score"] = df["cve_id"].map(lambda c: epss_data.get(c, {}).get("epss", 0))
    df["epss_percentile"] = df["cve_id"].map(lambda c: epss_data.get(c, {}).get("percentile", 0))

    def assign_priority(row):
        epss = row.get("epss_score", 0)
        cvss = row.get("cvss_score", 0)
        if epss > 0.7 and cvss >= 9.0:
            return "P0"
        if epss > 0.7 and cvss >= 7.0:
            return "P1"
        if epss > 0.4 and cvss >= 7.0:
            return "P2"
        if epss > 0.1 or cvss >= 7.0:
            return "P3"
        return "P4"

    df["priority"] = df.apply(assign_priority, axis=1)
    df = df.sort_values(["priority", "epss_score"], ascending=[True, False])
    df.to_csv(output_csv, index=False)
    print(f"[+] Prioritized {len(df)} vulnerabilities -> {output_csv}")
    print(f"    P0: {len(df[df['priority']=='P0'])}")
    print(f"    P1: {len(df[df['priority']=='P1'])}")
    print(f"    P2: {len(df[df['priority']=='P2'])}")
    print(f"    P3: {len(df[df['priority']=='P3'])}")
    print(f"    P4: {len(df[df['priority']=='P4'])}")
    return df

def fetch_epss_timeseries(cve_id):
    """Get historical EPSS scores for trend analysis."""
    resp = requests.get(
        "https://api.first.org/data/v1/epss",
        params={"cve": cve_id, "scope": "time-series"},
        timeout=30
    )
    if resp.status_code == 200:
        return resp.json().get("data", [])
    return []

def detect_epss_spikes(cve_id, threshold=0.3):
    """Detect significant EPSS score increases indicating emerging threats."""
    timeseries = fetch_epss_timeseries(cve_id)
    if len(timeseries) < 2:
        return False
    sorted_data = sorted(timeseries, key=lambda x: x.get("date", ""))
    latest = float(sorted_data[-1].get("epss", 0))
    previous = float(sorted_data[-2].get("epss", 0))
    increase = latest - previous
    if increase >= threshold:
        print(f"[!] EPSS spike detected for {cve_id}: {previous:.3f} -> {latest:.3f} (+{increase:.3f})")
        return True
    return False

Rationalization	Reality
"We are too small to be targeted"	Automated attacks target everyone. Size does not matter.
"Security slows us down"	A breach slows you down 100x more. Build security in from the start.
"We will fix it after launch"	Vulnerabilities in production are exploited within hours. Fix before deploy.