Webapp Implementing Epss Score For Vulnerability Prioritization

---
name: webapp-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.
domain: webapp
category: vulnerability-management
hide: true
license: Apache-2.0
metadata:
  manifest_entry: src/corpus/webapp-implementing-epss-score-for-vulnerability-prioritization/SKILL.md
---
# 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

- 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

## Common Misconfigurations & Verification

- **Stale EPSS data** — scores change daily. Verify the pipeline refreshes EPSS before each prioritization run.
- **EPSS used alone** — it predicts likelihood, not severity/impact. Verify it's combined with CVSS and asset context per the matrix.
- **Percentile vs probability confusion** — verify thresholds use the right field (`epss` vs `percentile`).
- **Missing CVEs scored as 0** — unmapped findings get silently deprioritized. Verify CVEs absent from the feed are flagged for review, not treated as low.
- **No spike detection** — verify trend/time-series checks catch a CVE whose EPSS jumps (emerging exploitation).
- **Batching limits** — verify >100-CVE queries are chunked so no findings are dropped.

## 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

### Query Single CVE
```bash
# 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",
#       "date": "2024-04-15"
#     }
#   ]
# }
```

### Query Multiple CVEs
```bash
# 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 Full EPSS Dataset
```bash
# 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
```

### Query Historical EPSS Scores
```bash
# 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"
```

## Prioritization Strategy

### EPSS + CVSS Combined Approach

| 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 |

### EPSS Percentile Thresholds
- **Top 1% (percentile >= 0.99)**: Extremely likely to be exploited; treat as Critical
- **Top 5% (percentile >= 0.95)**: High exploitation probability; prioritize remediation
- **Top 10% (percentile >= 0.90)**: Elevated risk; schedule for near-term remediation
- **Bottom 50%**: Low exploitation probability; handle in normal patch cycle

## Implementation

```python
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
```

## EPSS Trend Analysis

```python
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
```

## References

- [FIRST EPSS Official](https://www.first.org/epss/)
- [EPSS API Documentation](https://www.first.org/epss/api)
- [EPSS Model Documentation](https://www.first.org/epss/model)
- [EPSS Data Downloads](https://epss.cyentia.com/)
- [Cyentia Institute Research](https://www.cyentia.com/epss/)