[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EV5S4BMGZW2JSYFNAW1AXP":3,"skill-same:01M6EV5S4BMGZW2JSYFNAW1AXP":125},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":68,"commitSha":69,"skillDirectory":70,"forksCount":14,"repositoryHtmlUrl":71,"repositoryHomepageUrl":72,"ownerHtmlUrl":73,"ownerBlogUrl":6,"ownerDescription":74,"updatedAt":75,"install":76,"resources":89},"01M6EV5S4BMGZW2JSYFNAW1AXP","implementing-epss-score-for-vulnerability-prioritization",null,"Integrate FIRST's Exploit Prediction Scoring System (EPSS) API to prioritize vulnerability remediation based on real-world exploitation probability within 30 days.","henriquescastilho\u002Fmy-claude","henriquescastilho","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153296945?v=4","01M6ESH7EXPXTH9CVQ4K16SRG8","Henrique Scheer","01M6ESH7JHEK2PTNNH4HEH787Z",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-epss-score-for-vulnerability-prioritization\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"epss","implementing epss score for vulnerability prioritization","vulnerability","prioritization","machine","cybersecurity","id","probability","scoring","world","system","learning","api","exploit-prediction","first","vulnerability-prioritization","risk","machine-learning","exploitation","vulnerability-management","2026-11-10 10:12:30",false,"---\nname: implementing-epss-score-for-vulnerability-prioritization\ndescription: Integrate FIRST's Exploit Prediction Scoring System (EPSS) API to prioritize\n  vulnerability remediation based on real-world exploitation probability within 30\n  days.\ndomain: cybersecurity\nsubdomain: vulnerability-management\ntags:\n- epss\n- vulnerability-prioritization\n- first\n- exploit-prediction\n- cvss\n- risk-based\n- machine-learning\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- ID.RA-01\n- ID.RA-02\n- ID.IM-02\n- ID.RA-06\nmitre_attack:\n- T1190\n- T1203\n- T1068\n---\n\n# Implementing EPSS Score for Vulnerability Prioritization\n\n## Overview\n\nThe 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.\n\n\n## When to Use\n\n- When deploying or configuring implementing epss score for vulnerability prioritization capabilities in your environment\n- When establishing security controls aligned to compliance requirements\n- When building or improving security architecture for this domain\n- When conducting security assessments that require this implementation\n\n## Prerequisites\n\n- Python 3.9+ with `requests`, `pandas`, `matplotlib`\n- Access to FIRST EPSS API (https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss)\n- Vulnerability scan results with CVE identifiers\n- Optional: NVD API key for CVSS enrichment\n\n## EPSS API Usage\n\n### Query Single CVE\n```bash\n# Get EPSS score for a specific CVE\ncurl -s \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss?cve=CVE-2024-3400\" | python3 -m json.tool\n\n# Response:\n# {\n#   \"status\": \"OK\",\n#   \"status-code\": 200,\n#   \"version\": \"1.0\",\n#   \"total\": 1,\n#   \"data\": [\n#     {\n#       \"cve\": \"CVE-2024-3400\",\n#       \"epss\": \"0.95732\",\n#       \"percentile\": \"0.99721\",\n#       \"date\": \"2024-04-15\"\n#     }\n#   ]\n# }\n```\n\n### Query Multiple CVEs\n```bash\n# Batch query up to 100 CVEs\ncurl -s \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss?cve=CVE-2024-3400,CVE-2024-21887,CVE-2023-44228\" | \\\n  python3 -c \"\nimport sys, json\ndata = json.load(sys.stdin)\nfor item in data['data']:\n    pct = float(item['epss']) * 100\n    print(f\\\"{item['cve']}: {pct:.2f}% exploitation probability (percentile: {item['percentile']})\\\")\n\"\n```\n\n### Download Full EPSS Dataset\n```bash\n# Download complete daily EPSS scores (CSV format)\ncurl -s \"https:\u002F\u002Fepss.cyentia.com\u002Fepss_scores-current.csv.gz\" | gunzip > epss_scores_current.csv\n\n# Check size and preview\nwc -l epss_scores_current.csv\nhead -5 epss_scores_current.csv\n```\n\n### Query Historical EPSS Scores\n```bash\n# Get EPSS score for a specific date\ncurl -s \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss?cve=CVE-2024-3400&date=2024-04-12\"\n\n# Get time series data\ncurl -s \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss?cve=CVE-2024-3400&scope=time-series\"\n```\n\n## Prioritization Strategy\n\n### EPSS + CVSS Combined Approach\n\n| EPSS Score | CVSS Score | Priority | Action |\n|-----------|-----------|----------|--------|\n| > 0.7 | >= 9.0 | P0 - Immediate | Remediate within 24 hours |\n| > 0.7 | >= 7.0 | P1 - Urgent | Remediate within 48 hours |\n| > 0.4 | >= 7.0 | P2 - High | Remediate within 7 days |\n| > 0.1 | >= 4.0 | P3 - Medium | Remediate within 30 days |\n| \u003C= 0.1 | >= 7.0 | P3 - Medium | Remediate within 30 days |\n| \u003C= 0.1 | \u003C 7.0 | P4 - Low | Remediate within 90 days |\n\n### EPSS Percentile Thresholds\n- **Top 1% (percentile >= 0.99)**: Extremely likely to be exploited; treat as Critical\n- **Top 5% (percentile >= 0.95)**: High exploitation probability; prioritize remediation\n- **Top 10% (percentile >= 0.90)**: Elevated risk; schedule for near-term remediation\n- **Bottom 50%**: Low exploitation probability; handle in normal patch cycle\n\n## Implementation\n\n```python\nimport requests\nimport pandas as pd\nfrom datetime import datetime\n\ndef fetch_epss_scores(cve_list):\n    \"\"\"Fetch EPSS scores for a list of CVEs from FIRST API.\"\"\"\n    scores = {}\n    batch_size = 100\n    for i in range(0, len(cve_list), batch_size):\n        batch = cve_list[i:i + batch_size]\n        resp = requests.get(\n            \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss\",\n            params={\"cve\": \",\".join(batch)},\n            timeout=30\n        )\n        if resp.status_code == 200:\n            for entry in resp.json().get(\"data\", []):\n                scores[entry[\"cve\"]] = {\n                    \"epss\": float(entry[\"epss\"]),\n                    \"percentile\": float(entry[\"percentile\"]),\n                    \"date\": entry.get(\"date\", \"\"),\n                }\n    return scores\n\ndef prioritize_vulnerabilities(scan_results_csv, output_csv):\n    \"\"\"Enrich scan results with EPSS scores and assign priorities.\"\"\"\n    df = pd.read_csv(scan_results_csv)\n    cve_list = df[\"cve_id\"].dropna().unique().tolist()\n\n    epss_data = fetch_epss_scores(cve_list)\n\n    df[\"epss_score\"] = df[\"cve_id\"].map(lambda c: epss_data.get(c, {}).get(\"epss\", 0))\n    df[\"epss_percentile\"] = df[\"cve_id\"].map(lambda c: epss_data.get(c, {}).get(\"percentile\", 0))\n\n    def assign_priority(row):\n        epss = row.get(\"epss_score\", 0)\n        cvss = row.get(\"cvss_score\", 0)\n        if epss > 0.7 and cvss >= 9.0:\n            return \"P0\"\n        if epss > 0.7 and cvss >= 7.0:\n            return \"P1\"\n        if epss > 0.4 and cvss >= 7.0:\n            return \"P2\"\n        if epss > 0.1 or cvss >= 7.0:\n            return \"P3\"\n        return \"P4\"\n\n    df[\"priority\"] = df.apply(assign_priority, axis=1)\n    df = df.sort_values([\"priority\", \"epss_score\"], ascending=[True, False])\n    df.to_csv(output_csv, index=False)\n    print(f\"[+] Prioritized {len(df)} vulnerabilities -> {output_csv}\")\n    print(f\"    P0: {len(df[df['priority']=='P0'])}\")\n    print(f\"    P1: {len(df[df['priority']=='P1'])}\")\n    print(f\"    P2: {len(df[df['priority']=='P2'])}\")\n    print(f\"    P3: {len(df[df['priority']=='P3'])}\")\n    print(f\"    P4: {len(df[df['priority']=='P4'])}\")\n    return df\n```\n\n## EPSS Trend Analysis\n\n```python\ndef fetch_epss_timeseries(cve_id):\n    \"\"\"Get historical EPSS scores for trend analysis.\"\"\"\n    resp = requests.get(\n        \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss\",\n        params={\"cve\": cve_id, \"scope\": \"time-series\"},\n        timeout=30\n    )\n    if resp.status_code == 200:\n        return resp.json().get(\"data\", [])\n    return []\n\ndef detect_epss_spikes(cve_id, threshold=0.3):\n    \"\"\"Detect significant EPSS score increases indicating emerging threats.\"\"\"\n    timeseries = fetch_epss_timeseries(cve_id)\n    if len(timeseries) \u003C 2:\n        return False\n    sorted_data = sorted(timeseries, key=lambda x: x.get(\"date\", \"\"))\n    latest = float(sorted_data[-1].get(\"epss\", 0))\n    previous = float(sorted_data[-2].get(\"epss\", 0))\n    increase = latest - previous\n    if increase >= threshold:\n        print(f\"[!] EPSS spike detected for {cve_id}: {previous:.3f} -> {latest:.3f} (+{increase:.3f})\")\n        return True\n    return False\n```\n\n## References\n\n- [FIRST EPSS Official](https:\u002F\u002Fwww.first.org\u002Fepss\u002F)\n- [EPSS API Documentation](https:\u002F\u002Fwww.first.org\u002Fepss\u002Fapi)\n- [EPSS Model Documentation](https:\u002F\u002Fwww.first.org\u002Fepss\u002Fmodel)\n- [EPSS Data Downloads](https:\u002F\u002Fepss.cyentia.com\u002F)\n- [Cyentia Institute Research](https:\u002F\u002Fwww.cyentia.com\u002Fepss\u002F)\n","# Implementing EPSS Score for Vulnerability Prioritization\n\n## Overview\n\nThe 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.\n\n\n## When to Use\n\n- When deploying or configuring implementing epss score for vulnerability prioritization capabilities in your environment\n- When establishing security controls aligned to compliance requirements\n- When building or improving security architecture for this domain\n- When conducting security assessments that require this implementation\n\n## Prerequisites\n\n- Python 3.9+ with `requests`, `pandas`, `matplotlib`\n- Access to FIRST EPSS API (https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss)\n- Vulnerability scan results with CVE identifiers\n- Optional: NVD API key for CVSS enrichment\n\n## EPSS API Usage\n\n### Query Single CVE\n```bash\n# Get EPSS score for a specific CVE\ncurl -s \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss?cve=CVE-2024-3400\" | python3 -m json.tool\n\n# Response:\n# {\n#   \"status\": \"OK\",\n#   \"status-code\": 200,\n#   \"version\": \"1.0\",\n#   \"total\": 1,\n#   \"data\": [\n#     {\n#       \"cve\": \"CVE-2024-3400\",\n#       \"epss\": \"0.95732\",\n#       \"percentile\": \"0.99721\",\n#       \"date\": \"2024-04-15\"\n#     }\n#   ]\n# }\n```\n\n### Query Multiple CVEs\n```bash\n# Batch query up to 100 CVEs\ncurl -s \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss?cve=CVE-2024-3400,CVE-2024-21887,CVE-2023-44228\" | \\\n  python3 -c \"\nimport sys, json\ndata = json.load(sys.stdin)\nfor item in data['data']:\n    pct = float(item['epss']) * 100\n    print(f\\\"{item['cve']}: {pct:.2f}% exploitation probability (percentile: {item['percentile']})\\\")\n\"\n```\n\n### Download Full EPSS Dataset\n```bash\n# Download complete daily EPSS scores (CSV format)\ncurl -s \"https:\u002F\u002Fepss.cyentia.com\u002Fepss_scores-current.csv.gz\" | gunzip > epss_scores_current.csv\n\n# Check size and preview\nwc -l epss_scores_current.csv\nhead -5 epss_scores_current.csv\n```\n\n### Query Historical EPSS Scores\n```bash\n# Get EPSS score for a specific date\ncurl -s \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss?cve=CVE-2024-3400&date=2024-04-12\"\n\n# Get time series data\ncurl -s \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss?cve=CVE-2024-3400&scope=time-series\"\n```\n\n## Prioritization Strategy\n\n### EPSS + CVSS Combined Approach\n\n| EPSS Score | CVSS Score | Priority | Action |\n|-----------|-----------|----------|--------|\n| > 0.7 | >= 9.0 | P0 - Immediate | Remediate within 24 hours |\n| > 0.7 | >= 7.0 | P1 - Urgent | Remediate within 48 hours |\n| > 0.4 | >= 7.0 | P2 - High | Remediate within 7 days |\n| > 0.1 | >= 4.0 | P3 - Medium | Remediate within 30 days |\n| \u003C= 0.1 | >= 7.0 | P3 - Medium | Remediate within 30 days |\n| \u003C= 0.1 | \u003C 7.0 | P4 - Low | Remediate within 90 days |\n\n### EPSS Percentile Thresholds\n- **Top 1% (percentile >= 0.99)**: Extremely likely to be exploited; treat as Critical\n- **Top 5% (percentile >= 0.95)**: High exploitation probability; prioritize remediation\n- **Top 10% (percentile >= 0.90)**: Elevated risk; schedule for near-term remediation\n- **Bottom 50%**: Low exploitation probability; handle in normal patch cycle\n\n## Implementation\n\n```python\nimport requests\nimport pandas as pd\nfrom datetime import datetime\n\ndef fetch_epss_scores(cve_list):\n    \"\"\"Fetch EPSS scores for a list of CVEs from FIRST API.\"\"\"\n    scores = {}\n    batch_size = 100\n    for i in range(0, len(cve_list), batch_size):\n        batch = cve_list[i:i + batch_size]\n        resp = requests.get(\n            \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss\",\n            params={\"cve\": \",\".join(batch)},\n            timeout=30\n        )\n        if resp.status_code == 200:\n            for entry in resp.json().get(\"data\", []):\n                scores[entry[\"cve\"]] = {\n                    \"epss\": float(entry[\"epss\"]),\n                    \"percentile\": float(entry[\"percentile\"]),\n                    \"date\": entry.get(\"date\", \"\"),\n                }\n    return scores\n\ndef prioritize_vulnerabilities(scan_results_csv, output_csv):\n    \"\"\"Enrich scan results with EPSS scores and assign priorities.\"\"\"\n    df = pd.read_csv(scan_results_csv)\n    cve_list = df[\"cve_id\"].dropna().unique().tolist()\n\n    epss_data = fetch_epss_scores(cve_list)\n\n    df[\"epss_score\"] = df[\"cve_id\"].map(lambda c: epss_data.get(c, {}).get(\"epss\", 0))\n    df[\"epss_percentile\"] = df[\"cve_id\"].map(lambda c: epss_data.get(c, {}).get(\"percentile\", 0))\n\n    def assign_priority(row):\n        epss = row.get(\"epss_score\", 0)\n        cvss = row.get(\"cvss_score\", 0)\n        if epss > 0.7 and cvss >= 9.0:\n            return \"P0\"\n        if epss > 0.7 and cvss >= 7.0:\n            return \"P1\"\n        if epss > 0.4 and cvss >= 7.0:\n            return \"P2\"\n        if epss > 0.1 or cvss >= 7.0:\n            return \"P3\"\n        return \"P4\"\n\n    df[\"priority\"] = df.apply(assign_priority, axis=1)\n    df = df.sort_values([\"priority\", \"epss_score\"], ascending=[True, False])\n    df.to_csv(output_csv, index=False)\n    print(f\"[+] Prioritized {len(df)} vulnerabilities -> {output_csv}\")\n    print(f\"    P0: {len(df[df['priority']=='P0'])}\")\n    print(f\"    P1: {len(df[df['priority']=='P1'])}\")\n    print(f\"    P2: {len(df[df['priority']=='P2'])}\")\n    print(f\"    P3: {len(df[df['priority']=='P3'])}\")\n    print(f\"    P4: {len(df[df['priority']=='P4'])}\")\n    return df\n```\n\n## EPSS Trend Analysis\n\n```python\ndef fetch_epss_timeseries(cve_id):\n    \"\"\"Get historical EPSS scores for trend analysis.\"\"\"\n    resp = requests.get(\n        \"https:\u002F\u002Fapi.first.org\u002Fdata\u002Fv1\u002Fepss\",\n        params={\"cve\": cve_id, \"scope\": \"time-series\"},\n        timeout=30\n    )\n    if resp.status_code == 200:\n        return resp.json().get(\"data\", [])\n    return []\n\ndef detect_epss_spikes(cve_id, threshold=0.3):\n    \"\"\"Detect significant EPSS score increases indicating emerging threats.\"\"\"\n    timeseries = fetch_epss_timeseries(cve_id)\n    if len(timeseries) \u003C 2:\n        return False\n    sorted_data = sorted(timeseries, key=lambda x: x.get(\"date\", \"\"))\n    latest = float(sorted_data[-1].get(\"epss\", 0))\n    previous = float(sorted_data[-2].get(\"epss\", 0))\n    increase = latest - 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