Stats
Actions
Tags
Integrates FIRST EPSS API to fetch 30-day exploit probabilities for CVEs, prioritizing vulnerability remediation with CVSS-based risk strategies. Useful for vulnerability management workflows.
How this skill is triggered — by the user, by Claude, or both
Slash command
/cybersecurity-skills-zh:implementing-epss-score-for-vulnerability-prioritizationThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
漏洞利用预测评分系统（Exploit Prediction Scoring System，EPSS）是由 FIRST（事件响应和安全团队论坛）开发的数据驱动模型，用于估算 CVE 在未来 30 天内在真实环境中被利用的概率。EPSS 使用基于真实世界漏洞利用数据训练的机器学习模型，产生 0.0 到 1.0（即 0% 到 100%）的评分。与衡量严重性的 CVSS 不同，EPSS 衡量的是被利用的可能性，这使其成为基于风险的漏洞优先排序的关键工具。
漏洞利用预测评分系统（Exploit Prediction Scoring System，EPSS）是由 FIRST（事件响应和安全团队论坛）开发的数据驱动模型，用于估算 CVE 在未来 30 天内在真实环境中被利用的概率。EPSS 使用基于真实世界漏洞利用数据训练的机器学习模型，产生 0.0 到 1.0（即 0% 到 100%）的评分。与衡量严重性的 CVSS 不同，EPSS 衡量的是被利用的可能性，这使其成为基于风险的漏洞优先排序的关键工具。
requests、pandas、matplotlib# 获取特定 CVE 的 EPSS 评分
curl -s "https://api.first.org/data/v1/epss?cve=CVE-2024-3400" | python3 -m json.tool
# 响应示例：
# {
# "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"
# }
# ]
# }
# 最多批量查询 100 个 CVE
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}% 利用概率（百分位数：{item['percentile']}）\")
"
# 下载完整的每日 EPSS 评分（CSV 格式）
curl -s "https://epss.cyentia.com/epss_scores-current.csv.gz" | gunzip > epss_scores_current.csv
# 检查大小和预览
wc -l epss_scores_current.csv
head -5 epss_scores_current.csv
# 获取特定日期的 EPSS 评分
curl -s "https://api.first.org/data/v1/epss?cve=CVE-2024-3400&date=2024-04-12"
# 获取时间序列数据
curl -s "https://api.first.org/data/v1/epss?cve=CVE-2024-3400&scope=time-series"
| EPSS 评分 | CVSS 评分 | 优先级 | 行动 |
|---|---|---|---|
| > 0.7 | >= 9.0 | P0 - 立即 | 24 小时内修复 |
| > 0.7 | >= 7.0 | P1 - 紧急 | 48 小时内修复 |
| > 0.4 | >= 7.0 | P2 - 高 | 7 天内修复 |
| > 0.1 | >= 4.0 | P3 - 中 | 30 天内修复 |
| <= 0.1 | >= 7.0 | P3 - 中 | 30 天内修复 |
| <= 0.1 | < 7.0 | P4 - 低 | 90 天内修复 |
import requests
import pandas as pd
from datetime import datetime
def fetch_epss_scores(cve_list):
"""从 FIRST API 批量获取 CVE 列表的 EPSS 评分。"""
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):
"""用 EPSS 评分丰富扫描结果并分配优先级。"""
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"[+] 已对 {len(df)} 个漏洞进行优先排序 -> {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):
"""获取历史 EPSS 评分用于趋势分析。"""
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):
"""检测 EPSS 评分显著上升，指示新兴威胁。"""
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"[!] 检测到 {cve_id} 的 EPSS 激增：{previous:.3f} -> {latest:.3f} (+{increase:.3f})")
return True
return False
npx claudepluginhub killvxk/cybersecurity-skills-zhIntegrates FIRST's EPSS API to prioritize vulnerability remediation based on real-world exploitation probability within 30 days. Use when triaging CVEs or building risk-based patching workflows.
Integrates FIRST EPSS API to prioritize CVEs by real-world 30-day exploitation probability using Python and curl. Guides queries, batch processing, and dataset downloads for vulnerability remediation.
Integrates FIRST EPSS API to prioritize CVEs by real-world 30-day exploitation probability using Python and curl. Guides queries, batch processing, and dataset downloads for vulnerability remediation.