Security data from 24 sources represented as Subject-Predicate-Object (SPO) triples in Parquet format, ready for knowledge-graph construction, graph-ML, RAG pipelines, and threat-intelligence analysis.

Sources: ATT&CK · CAPEC · CWE · CVE · CPE · D3FEND · ATLAS · CAR · ENGAGE · F3 · EPSS · KEV · Vulnrichment · GHSA · Sigma · ExploitDB · MISP Galaxies · LOLBAS · LOLDrivers · Atomic Red Team · NIST 800-53 · Nuclei · EUVD · OSV

Last updated: 2026-07-13T09:12:52Z

Quick Start

from datasets import load_dataset

ds = load_dataset("s0u9ata/security-kg", "enterprise")
print(ds["train"][0])
# {'subject': 'T1059.001', 'predicate': 'rdf:type', 'object': 'Technique', 'source': 'attack', 'object_type': 'enum', 'meta': ''}

Configurations

Config	Description	Est. Triples	Status
`enterprise` (default)	Enterprise ATT&CK	43,739	Current
`mobile`	Mobile ATT&CK	5,579	Current
`ics`	ICS ATT&CK	4,266	Current
`attack-all`	ATT&CK combined (deduplicated)	51,907	Current
`capec`	CAPEC attack patterns	8,114	Current
`cwe`	CWE weaknesses	14,583	Current
`cve`	CVE vulnerabilities	3,875,509	Current
`cpe`	CPE platform enumeration	13,545,566	Last good version
`d3fend`	D3FEND defensive techniques	8,154	Current
`atlas`	ATLAS AI/ML techniques	1,373	Current
`car`	CAR analytics	1,617	Current
`engage`	ENGAGE adversary engagement	1,464	Current
`f3`	F3 fraud techniques & tactics	1,053	Current
`epss`	EPSS exploit prediction scores	694,384	Current
`kev`	KEV known exploited vulns	17,938	Current
`vulnrichment`	CISA Vulnrichment (SSVC, CVSS, CWE enrichment)	1,549,549	Current
`ghsa`	GitHub Security Advisories	382,938	Current
`sigma`	Sigma detection rules	33,498	Current
`exploitdb`	ExploitDB public exploits	347,263	Current
`misp_galaxy`	MISP Galaxy threat intelligence clusters	203,459	Current
`lolbas`	LOLBAS living-off-the-land binaries	3,692	Current
`loldrivers`	LOLDrivers vulnerable/malicious drivers	11,738	Current
`atomic`	Atomic Red Team test definitions	11,024	Current
`nist_800_53`	NIST 800-53 → ATT&CK control mappings	4,786	Current
`nuclei`	Nuclei vulnerability detection templates	98,515	Current
`euvd`	EUVD European vulnerability database	4,923	Current
`osv`	OSV open-source vulnerabilities (23 ecosystems)	7,202,861	Current
`combined`	All sources merged (deduplicated)	28,075,908	Current

Note: cpe failed conversion and uses its last known good version. The combined config includes this fallback version.

Knowledge Graph Structure

     Group  Campaign
        \      /
          uses
           |
           v
      TECHNIQUE -----> Tactic
        ^  ^  ^
        |  |  |
        |  |  +-- D3FEND (counters)
        |  |  +-- CAR (detects)
        |  |  +-- Sigma (detects)
        |  |  +-- ENGAGE (engages)
        |  |  +-- F3 (fraud techniques)
        |  |  +-- ATLAS (related)
        |  |  +-- MISP Galaxies (cross-refs)
        |  |  +-- LOLBAS (maps-to)
        |  |  +-- LOLDrivers (maps-to)
        |  |  +-- Atomic Red Team (tests)
        |  |  +-- NIST 800-53 (mitigates)
        |  |
        |  +-- Mitigation (mitigates)
        |  +-- DataComponent (detects)
        |
        +-- maps-to -- CAPEC
                         |
                  related-weakness
                         |
                         v
                        CWE
                         ^
                         |
                  related-weakness
                         |
                        CVE ----> CPE
                         ^
                         |
                   EPSS (score)
                   KEV (exploited)
                   GHSA (advisory)
                   Vulnrichment (SSVC)
                   ExploitDB (exploit)
                   Nuclei (detection template)
                   EUVD (EU advisory)
                   OSV (open-source vuln)

Schema

Each row is an enriched triple with six string columns:

Column	Description	Examples
`subject`	Entity ID	`T1059.001`, `G0016`, `CAPEC-66`, `CWE-79`, `CVE-2024-1234`, `cpe:2.3:a:apache:httpd:*`, `D3-FE`, `AML.T0000`, `CAR-2024-01-001`, `EAC0001`, `GHSA-xxxx-yyyy-zzzz`, `EDB-16929`, `Msbuild.exe`, `EUVD-2025-4893`, `AC-2`, `PYSEC-2024-1234`
`predicate`	Property name or relationship type	`rdf:type`, `name`, `uses`, `mitigates`, `epss-score`, `counters`, `ssvc-exploitation`, `exploits-cve`, `detects-technique`
`object`	Value or target entity ID	`Technique`, `PowerShell`, `T1059`, `CWE-89`, `0.97500`, `SecurityAdvisory`, `SigmaRule`, `Exploit`
`source`	Originating dataset	`attack`, `cve`, `cwe`, `capec`, `epss`, `kev`, `ghsa`, `sigma`, `d3fend`, `atlas`, `car`, `engage`, `f3`, `cpe`, `vulnrichment`, `exploitdb`, `misp_galaxy`, `lolbas`, `loldrivers`, `atomic`, `nist_800_53`, `nuclei`, `euvd`, `osv`
`object_type`	Value type of the object	`string`, `id`, `enum`, `date`, `number`, `boolean`, `url`
`meta`	Supplemental JSON metadata (empty string if none)	`{"references":["https://..."],"credits":[...]}`, `{"cvss_vector":"...","cvss_version":"3.1"}`

Predicate Reference

ATT&CK Entity Properties

Predicate	Description	Example object value
`rdf:type`	Entity type	`Technique`, `Group`, `Malware`, `Tool`, `Tactic`, `Mitigation`, `Campaign`, `DataSource`, `DataComponent`
`name`	Display name	`PowerShell`
`description`	Full description text	`Adversaries may abuse PowerShell...`
`platform`	Applicable platform	`Windows`, `Linux`, `macOS`
`domain`	ATT&CK domain	`enterprise-attack`
`alias`	Alternative name	`Cozy Bear`
`is-subtechnique`	Whether entity is a sub-technique	`True`, `False`
`belongs-to-tactic`	Tactic ATT&CK ID	`TA0002`
`shortname`	Tactic shortname	`credential-access`
`url`	ATT&CK website URL	`https://attack.mitre.org/techniques/T1059/001`
`created` / `modified`	Timestamps	`2020-01-14 17:18:32...`

ATT&CK Relationship Predicates

Predicate	Typical subject / object	Example
`uses`	Group/Campaign/Software / Technique	`G0016 / T1059.001`
`mitigates`	Mitigation / Technique	`M1049 / T1059.001`
`subtechnique-of`	Sub-technique / Parent technique	`T1059.001 / T1059`
`detects`	DataComponent / Technique	`DC0001 / T1059.001`
`attributed-to`	Campaign / Group	`C0018 / G0016`

CAPEC Predicates

Predicate	Description	Example object value
`rdf:type`	`AttackPattern`	`AttackPattern`
`name` / `description`	Display name / full text	`SQL Injection`
`abstraction` / `status`	Level / status	`Standard`, `Stable`
`likelihood` / `severity`	Attack likelihood / severity	`High`
`child-of`	Parent attack pattern	`CAPEC-248`
`related-weakness`	Related CWE	`CWE-89`
`maps-to-technique`	Mapped ATT&CK technique	`T1190.002`

CWE Predicates

Predicate	Description	Example object value
`rdf:type`	`Weakness`	`Weakness`
`name` / `description`	Display name / full text	`Cross-site Scripting (XSS)`
`abstraction` / `status`	Level / status	`Base`, `Stable`
`likelihood-of-exploit`	Exploitation likelihood	`High`
`child-of`	Parent weakness	`CWE-74`
`related-attack-pattern`	Related CAPEC	`CAPEC-86`
`platform`	Applicable platform	`JavaScript`
`consequence-scope` / `consequence-impact`	Impact	`Confidentiality`, `Read Data`
`introduction-phase`	Introduction phase	`Implementation`

CVE Predicates

Predicate	Description	Example object value
`rdf:type`	`Vulnerability`	`Vulnerability`
`state`	CVE state	`PUBLISHED`
`description`	English description	`A remote code execution...`
`date-published` / `date-updated`	Timestamps	`2024-01-15T00:00:00.000Z`
`assigner`	Assigning organization	`microsoft`
`vendor` / `product`	Affected vendor/product	`Microsoft`, `Windows`
`affects-cpe`	Affected CPE string	`cpe:2.3:o:microsoft:windows_10:*`
`platform`	Affected platform	`x64`
`related-weakness`	Related CWE	`CWE-79`
`cvss-base-score` / `cvss-severity`	CVSS metrics	`9.8`, `CRITICAL`

CPE Predicates

Predicate	Description	Example object value
`rdf:type`	`Platform`	`Platform`
`part`	CPE part type	`application`, `operating_system`, `hardware`
`vendor` / `product` / `version`	Components	`apache`, `httpd`, `2.4.51`
`title`	English display name	`Apache HTTP Server 2.4.51`
`created` / `modified`	Timestamps	`2021-10-07`

D3FEND Predicates

Predicate	Description	Example object value
`rdf:type`	`DefensiveTechnique` or `OffensiveTechnique`	`DefensiveTechnique`
`name` / `definition`	Display name / definition	`File Encryption`
`synonym`	Alternative name	`Disk Encryption`
`child-of`	Parent technique	`PlatformHardening`
`counters`	Countered offensive technique	`T1059`

ATLAS Predicates

Predicate	Description	Example object value
`rdf:type`	`Tactic`, `Technique`, `CaseStudy`, `Mitigation`	`Technique`
`name` / `description`	Display name / full text	`ML Supply Chain Compromise`
`maturity`	Technique maturity	`Reviewed`
`belongs-to-tactic`	Parent tactic	`AML.TA0001`
`subtechnique-of`	Parent technique	`AML.T0000`
`related-attack-technique`	Linked ATT&CK technique	`T1195`
`related-attack-tactic`	Linked ATT&CK tactic	`TA0001`
`uses-technique`	Case study technique	`AML.T0000`
`mitigates`	Mitigated technique	`AML.T0000`

CAR Predicates

Predicate	Description	Example object value
`rdf:type`	`Analytic`	`Analytic`
`title` / `description`	Analytic name / full text	`Suspicious PowerShell Commands`
`platform`	Applicable platform	`Windows`
`information-domain`	Information domain	`Host`
`analytic-type`	Type of analytic	`Situational Awareness`
`detects-technique`	Detected ATT&CK technique	`T1059`
`detects-subtechnique`	Detected subtechnique	`T1059.001`
`covers-tactic`	Covered ATT&CK tactic	`Execution`
`maps-to-d3fend`	Linked D3FEND technique	`D3-PSA`

ENGAGE Predicates

Predicate	Description	Example object value
`rdf:type`	`EngagementActivity` or `AdversaryVulnerability`	`EngagementActivity`
`name` / `description`	Display name / full text	`Software Manipulation`
`engages-technique`	Engaged ATT&CK technique	`T1001`
`vulnerability-of`	ATT&CK technique this adversary vulnerability applies to	`T1001`
`addresses-vulnerability`	Addressed adversary vulnerability	`EAV0001`

F3 Predicates

Predicate	Description	Example object value
`rdf:type`	`Tactic` or `Technique`	`Technique`
`name` / `description`	Display name / full text	`Account Takeover`
`shortname`	Tactic shortname	`positioning`, `monetization`
`is-subtechnique`	Whether entity is a sub-technique	`true`
`belongs-to-tactic`	Parent tactic	`FA0001`
`subtechnique-of`	Parent technique	`F1001`
`url`	F3 website URL	`https://ctid.mitre.org/fraud/techniques/F1001`
`created` / `modified`	Timestamps	`2026-04-02T19:15:57.686Z`

EPSS Predicates

Predicate	Description	Example object value
`epss-score`	Exploit probability (0-1)	`0.97500`
`epss-percentile`	Score percentile (0-1)	`0.99900`

KEV Predicates

Predicate	Description	Example object value
`rdf:type`	`KnownExploitedVulnerability`	`KnownExploitedVulnerability`
`kev-vendor` / `kev-product`	Affected vendor/product	`Microsoft`, `Windows`
`kev-name` / `kev-description`	Vulnerability name/description	`Windows Privilege Escalation`
`kev-date-added` / `kev-due-date`	Dates	`2024-01-15`
`kev-required-action`	Required remediation action	`Apply updates per vendor instructions.`
`kev-ransomware-use`	Ransomware campaign use	`Known`, `Unknown`
`related-weakness`	Related CWE	`CWE-269`

Vulnrichment Predicates

Predicate	Description	Example object value
`ssvc-exploitation`	SSVC exploitation status	`active`, `poc`, `none`
`ssvc-automatable`	Whether exploitation is automatable	`yes`, `no`
`ssvc-technical-impact`	Technical impact level	`total`, `partial`
`adp-cvss-base-score`	CISA-analyzed CVSS base score	`9.8`
`adp-cvss-severity`	CISA-analyzed CVSS severity	`CRITICAL`
`adp-related-weakness`	CISA-assigned CWE	`CWE-79`
`adp-affects-cpe`	CISA-assigned CPE	`cpe:2.3:o:microsoft:windows_10:*`

GHSA Predicates

Predicate	Description	Example object value
`rdf:type`	`SecurityAdvisory`	`SecurityAdvisory`
`summary`	Advisory summary	`XSS vulnerability in example-package`
`date-published` / `date-modified`	Timestamps	`2024-01-15T00:00:00Z`
`severity`	Severity level	`HIGH`, `MODERATE`, `LOW`, `CRITICAL`
`related-cve`	Associated CVE	`CVE-2024-1234`
`related-weakness`	Associated CWE	`CWE-79`
`cvss-vector`	CVSS v3 vector string	`CVSS:3.1/AV:N/AC:L/...`
`affects-package`	Affected package (ecosystem/name)	`npm/example-package`
`fixed-in`	Fixed version for package (ecosystem/name@version)	`npm/example-package@2.0.1`

Sigma Predicates

Predicate	Description	Example object value
`rdf:type`	`SigmaRule`	`SigmaRule`
`title` / `description`	Rule name / full text	`Suspicious PowerShell Download`
`status`	Rule maturity	`stable`, `test`, `experimental`
`level`	Detection severity	`critical`, `high`, `medium`, `low`, `informational`
`author` / `date`	Rule author / creation date	`Security Researcher`, `2024-01-15`
`logsource-category`	Log source category	`process_creation`, `network_connection`
`logsource-product`	Log source product	`windows`, `linux`
`logsource-service`	Log source service	`sshd`, `sysmon`
`detects-technique`	Detected ATT&CK technique	`T1059.001`
`related-cve`	Related CVE	`CVE-2024-1234`

ExploitDB Predicates

Predicate	Description	Example object value
`rdf:type`	`Exploit`	`Exploit`
`description`	Exploit description	`Apache HTTP Server RCE`
`date-published`	Publication date	`2024-01-15`
`author`	Exploit author	`Metasploit`
`exploit-type`	Exploit category	`remote`, `local`, `dos`, `webapps`
`platform`	Target platform	`linux`, `windows`, `aix`
`verified`	Verified by OffSec	`True`
`exploits-cve`	Exploited CVE	`CVE-2024-1234`

MISP Galaxy Predicates

Predicate	Description	Example object value
`rdf:type`	Galaxy entity type	`ThreatActor`, `Ransomware`, `Botnet`, `RAT`
`name`	Display name	`APT1`
`description`	Full description	(text)
`galaxy`	Galaxy cluster type	`threat-actor`, `ransomware`
`synonym`	Alternative name	`Comment Crew`
`country`	Country code (ISO 3166-1)	`CN`
`cfr-suspected-state-sponsor`	Suspected state sponsor	`China`
`targets-country`	Targeted country	`United States`
`targets-sector`	Targeted sector	`Government`
`attribution-confidence`	Confidence level	`50`
`similar-to`	Similar/duplicate entity	`misp:<uuid>`
`uses`	Uses technique/tool	`misp:<uuid>`
`used-by`	Used by actor	`misp:<uuid>`
`variant-of`	Variant relationship	`misp:<uuid>`
`targets`	Targets entity	`misp:<uuid>`
`attributed-to`	Attributed to entity	`misp:<uuid>`
`misp-related`	Generic relationship	`misp:<uuid>`
`related-attack-id`	Cross-link to ATT&CK	`T1059.001`, `G0006`

LOLBAS Predicates

Predicate	Description	Example object value
`rdf:type`	`LOLBinary`	`LOLBinary`
`name`	Binary display name	`Msbuild.exe`
`description`	Binary description	`Used to compile and execute code`
`maps-to-technique`	Mapped ATT&CK technique	`T1127.001`
`category`	Use category	`Execute`, `Download`
`usecase`	Use case description	`Compile and run code`
`privileges`	Required privileges	`User`
`platform`	Target platform	`Windows`
`full-path`	File system path	`C:\Windows\Microsoft.NET\...`

LOLDrivers Predicates

Predicate	Description	Example object value
`rdf:type`	`LOLDriver`	`LOLDriver`
`name`	Driver name	`RTCore64.sys`
`category`	Driver category	`vulnerable driver`, `malicious driver`
`maps-to-technique`	Mapped ATT&CK technique	`T1068`
`usecase`	Use case description	`Exploits`
`privileges`	Required privileges	`kernel`
`platform`	Target platform	`Windows`
`sha256` / `sha1` / `md5`	Sample hashes	`01...af`
`vendor` / `product`	Driver vendor/product	`Micro-Star Int'l Co.`, `RTCore64`

Atomic Red Team Predicates

Predicate	Description	Example object value
`rdf:type`	`AtomicTest`	`AtomicTest`
`name`	Test name	`Mimikatz - Cred Dump`
`description`	Test description	`Runs Mimikatz to dump credentials`
`tests-technique`	Tested ATT&CK technique	`T1003.001`
`platform`	Supported platform	`windows`, `linux`, `macos`
`executor`	Execution method	`powershell`, `sh`, `command_prompt`

NIST 800-53 Predicates

Predicate	Description	Example object value
`rdf:type`	`SecurityControl`	`SecurityControl`
`name`	Control name	`Account Management`
`description`	Control description	`Manage system accounts...`
`control-family`	Control family	`AC`, `SI`, `AU`
`mitigates-technique`	Mitigated ATT&CK technique	`T1078`

Nuclei Predicates

Predicate	Description	Example object value
`rdf:type`	`NucleiTemplate`	`NucleiTemplate`
`name`	Template name	`Apache Struts2 RCE`
`description`	Template description	`Detects Apache Struts2 RCE...`
`severity`	Detection severity	`critical`, `high`, `medium`, `low`, `info`
`author`	Template author	`pdteam`
`related-weakness`	Related CWE	`CWE-94`
`related-cve`	Related CVE	`CVE-2023-1234`
`cvss-base-score`	CVSS base score	`9.8`
`cvss-vector`	CVSS vector string	`CVSS:3.1/AV:N/AC:L/...`

EUVD Predicates

Predicate	Description	Example object value
`rdf:type`	`EUVulnerability`	`EUVulnerability`
`description`	Vulnerability description	`A remote code execution...`
`date-published`	Publication date	`2025-01-15`
`cvss-base-score`	CVSS base score	`9.8`
`cvss-vector`	CVSS vector string	`CVSS:3.1/AV:N/AC:L/...`
`epss-score`	EPSS score	`0.95`
`related-cve`	Related CVE	`CVE-2025-1234`
`vendor` / `product`	Affected vendor/product	`Apache`, `HTTP Server`

OSV Predicates

Predicate	Description	Example object value
`rdf:type`	`OSVulnerability`	`OSVulnerability`
`summary`	Vulnerability summary	`XSS in example-package`
`date-published` / `date-modified`	Timestamps	`2024-01-15T00:00:00Z`
`related-cve`	Related CVE	`CVE-2024-1234`
`related-weakness`	Related CWE	`CWE-79`
`affects-package`	Affected package (ecosystem/name)	`PyPI/requests`
`ecosystem`	Package ecosystem	`PyPI`, `npm`, `Go`, `crates.io`
`cvss-vector`	CVSS vector string	`CVSS:3.1/AV:N/AC:L/...`

Dataset Creation

Source Data

Source	Feed	Format
ATT&CK	`mitre-attack/attack-stix-data`	STIX 2.0 JSON
CAPEC	`capec_latest.xml`	XML
CWE	`cwec_latest.xml.zip`	XML (ZIP)
CVE	`CVEProject/cvelistV5`	JSON 5.x (ZIP)
CPE	`nvdcpe-2.0.tar.gz`	JSON (tar.gz)
D3FEND	`d3fend.json`	JSON-LD
ATLAS	`ATLAS.yaml`	YAML
CAR	`mitre-attack/car`	YAML (ZIP)
ENGAGE	`attack_mapping.json`	JSON
F3	`fight-fraud-framework`	STIX 2.1 JSON
EPSS	`epss_scores-current.csv.gz`	CSV (gzip)
KEV	`known_exploited_vulnerabilities.json`	JSON
Vulnrichment	`cisagov/vulnrichment`	JSON 5.x (ZIP)
GHSA	`github/advisory-database`	OSV JSON (ZIP)
Sigma	`SigmaHQ/sigma`	YAML (ZIP)
ExploitDB	`files_exploits.csv`	CSV
MISP Galaxies	`MISP/misp-galaxy`	JSON (ZIP)
LOLBAS	`LOLBAS-Project/LOLBAS`	YAML (ZIP)
LOLDrivers	`magicsword-io/LOLDrivers`	YAML (ZIP)
Atomic Red Team	`redcanaryco/atomic-red-team`	YAML (ZIP)
NIST 800-53	`mappings-explorer`	JSON (ZIP)
Nuclei	`projectdiscovery/nuclei-templates`	YAML (ZIP)
EUVD	`euvdservices.enisa.europa.eu`	JSON
OSV	`osv.dev`	JSON (per-ecosystem ZIPs)

Conversion Pipeline

The converter downloads source data, extracts entity property triples and relationship triples, and writes them as Parquet files. The source code and full documentation are at:

github.com/S0UGATA/security-kg

To regenerate or update this dataset:

git clone https://github.com/S0UGATA/security-kg.git
cd security-kg
pip install -r requirements.txt
python src/convert.py

This produces fresh Parquet files in output/ from the latest data across all 24 sources.

Visualizer

Explore the Parquet files interactively at security-kg-viz.

Pre-computed neighborhoods (`neighborhoods/`)

For the most-connected entities in combined.parquet, this dataset ships pre-rendered multi-hop neighborhood JSONs the visualizer can fetch directly, skipping the DuckDB-WASM + Parquet path entirely for hot lookups:

neighborhoods/
  T1059.json              # array of Triple objects (depth=2, limit=500)
  T1059.001.json
  CVE-2024-1234.json
  CAPEC-100.json
  ...
  index.json              # { source, fingerprint, depth, limit, entities: [...] }

Each <entity>.json is the same shape the viz already builds from q.entityNeighborhood() — an array of {subject, predicate, object, source, object_type, object_canonical}. Filenames use a reversible slug (characters outside [A-Za-z0-9._-] are _xx hex-escaped); index.json is the authoritative mapping from entity → filename and includes a parquet fingerprint for cache invalidation. The bundle is regenerated each weekly refresh whenever combined.parquet changes.

Use Cases

Knowledge Graph Construction: Load triples into Neo4j, RDFLib, or NetworkX for graph queries
Graph ML: Train graph neural networks (GNNs) on security data structure for link prediction
RAG / LLM Grounding: Use triples as structured context for retrieval-augmented generation
Threat Intelligence: Query relationships between groups, techniques, vulnerabilities, and mitigations
Vulnerability Prioritization: Combine SSVC, EPSS, KEV, and ExploitDB data for risk-based triage
Defensive Gap Analysis: Find heavily-used ATT&CK techniques with insufficient detection coverage
Supply Chain Risk: Score open-source packages by linking GHSA advisories to CVE/EPSS/KEV enrichment
Security Automation: Programmatically map detections to techniques to tactics

Cross-Source Analysis Notebook

The repository includes a Jupyter notebook with 16 cross-source analyses and visualizations built on combined.parquet — covering SSVC patch prioritization, defensive gap analysis, kill chain tactic coverage, exploit weaponization timelines, ransomware CWE pipelines, supply chain package risk, and more.

Example Queries

SSVC Patch Prioritization (Vulnrichment + EPSS + KEV)

import pandas as pd
from datasets import load_dataset

# Load combined graph for cross-source queries
ds = load_dataset("s0u9ata/security-kg", "combined")
df = ds["train"].to_pandas()

# Build SSVC triage matrix: exploitation status × automatable × EPSS score
ssvc = df[df.predicate == "ssvc-exploitation"][["subject", "object"]].rename(columns={"object": "exploitation"})
auto = df[df.predicate == "ssvc-automatable"][["subject", "object"]].rename(columns={"object": "automatable"})
epss = df[df.predicate == "epss-score"][["subject", "object"]].copy()
epss["epss"] = epss.object.astype(float)

triage = ssvc.merge(auto, on="subject").merge(epss[["subject", "epss"]], on="subject")

# Highest priority: actively exploited + automatable + high EPSS
critical = triage[(triage.exploitation == "active") & (triage.automatable == "yes") & (triage.epss > 0.9)]
print(f"Immediate action: {len(critical)} CVEs")

Defensive Gap Analysis (ATT&CK + Sigma + D3FEND + CAR)

# Find ATT&CK techniques heavily used by APT groups but poorly covered by detections
uses = df[(df.predicate == "uses") & df.subject.str.startswith("G")]
group_usage = uses.groupby("object").subject.nunique().rename("groups_using")

# Count detection sources per technique (Sigma + CAR + D3FEND + ENGAGE)
sigma = df[df.predicate == "detects-technique"].groupby("object").subject.nunique().rename("detections")
d3fend = df[df.predicate == "restricts"].groupby("object").subject.nunique().rename("defenses")

coverage = pd.DataFrame(group_usage).join(sigma).join(d3fend).fillna(0)
gaps = coverage[(coverage.groups_using > 10) & (coverage.detections < 5)]
print(f"High-usage, low-detection techniques: {len(gaps)}")

Supply Chain Risk (GHSA + CVE + EPSS + KEV + ExploitDB)

# Score open-source packages by aggregating risk from linked CVEs
ghsa_cve = df[df.predicate == "related-cve"][["subject", "object"]].rename(columns={"subject": "ghsa", "object": "cve"})
packages = df[df.predicate == "affects-package"][["subject", "object"]].rename(columns={"subject": "ghsa", "object": "pkg"})

epss_scores = df[df.predicate == "epss-score"][["subject", "object"]].copy()
epss_scores["epss"] = epss_scores.object.astype(float)

kev_cves = set(df[(df.predicate == "rdf:type") & (df.object == "KnownExploitedVulnerability")].subject)
exploit_cves = set(df[df.predicate == "exploits-cve"].object)

# Join package → GHSA → CVE → enrichment
risk = packages.merge(ghsa_cve, on="ghsa").merge(epss_scores[["subject", "epss"]], left_on="cve", right_on="subject")
risk["in_kev"] = risk.cve.isin(kev_cves)
risk["has_exploit"] = risk.cve.isin(exploit_cves)
risk["ecosystem"] = risk.pkg.str.split("/").str[0]

# Top ecosystems by high-risk CVE count
high_risk = risk[(risk.epss > 0.5) | risk.in_kev | risk.has_exploit]
print(high_risk.groupby("ecosystem").cve.nunique().sort_values(ascending=False).head(10))

CAPEC → CWE → CVE (Attack Pattern Chain)

capec = load_dataset("s0u9ata/security-kg", "capec")["train"].to_pandas()
cve = load_dataset("s0u9ata/security-kg", "cve")["train"].to_pandas()

# Find CWEs related to SQL Injection (CAPEC-66)
cwe_ids = capec[(capec.subject == "CAPEC-66") & (capec.predicate == "related-weakness")].object.tolist()

# Find CVEs with those CWEs
for cwe_id in cwe_ids:
    related_cves = cve[(cve.predicate == "related-weakness") & (cve.object == cwe_id)].subject.unique()
    print(f"{cwe_id}: {len(related_cves)} CVEs")

D3FEND (Defensive Taxonomy)

ds = load_dataset("s0u9ata/security-kg", "d3fend")
df = ds["train"].to_pandas()

# All 497 defensive techniques in the D3FEND taxonomy
defenses = df[(df.predicate == "rdf:type") & (df.object == "DefensiveTechnique")]
print(f"Defensive techniques: {len(defenses)}")

# Find children of a category (e.g., all techniques under Network Traffic Analysis)
children = df[(df.predicate == "child-of") & (df.object == "NetworkTrafficAnalysis")].subject.tolist()

# Get their names
names = df[df.predicate == "name"][["subject", "object"]]
print(names[names.subject.isin(children)].to_string(index=False))

Source Licensing & Attribution

This dataset is published under the Apache 2.0 license. The underlying source data is provided under various licenses as detailed below. By using this dataset, you agree to comply with each source's respective terms.

Source	License	Attribution
ATT&CK	Custom royalty-free (MITRE)	© The MITRE Corporation. Reproduced and distributed with the permission of The MITRE Corporation.
CAPEC	Custom royalty-free (MITRE)	© The MITRE Corporation. Reproduced and distributed with the permission of The MITRE Corporation.
CWE	Custom royalty-free (MITRE)	© The MITRE Corporation. Reproduced and distributed with the permission of The MITRE Corporation.
CVE	Custom permissive (MITRE)	© The MITRE Corporation. CVE® is a registered trademark of The MITRE Corporation.
CPE / NVD	Public domain (NIST)	This product uses data from the NVD API but is not endorsed or certified by the NVD.
D3FEND	MIT License	© The MITRE Corporation. MITRE D3FEND™ is a trademark of The MITRE Corporation.
ATLAS	Apache 2.0	© MITRE.
CAR	Apache 2.0	© The MITRE Corporation.
ENGAGE	Apache 2.0 (GitHub repo) / Custom restrictive (website ToU)	© The MITRE Corporation. Reproduced and distributed with the permission of The MITRE Corporation. Note: the GitHub repo is licensed Apache 2.0, but the website terms restrict use to internal/non-commercial purposes. Clarification pending with MITRE.
F3	Apache 2.0	© MITRE Engenuity, Center for Threat-Informed Defense.
EPSS	Custom permissive (FIRST)	Jacobs, Romanosky, Edwards, Roytman, Adjerid (2021), Exploit Prediction Scoring System, Digital Threats Research and Practice, 2(3). See first.org/epss.
KEV	Public domain (U.S. Gov)	Source: CISA Known Exploited Vulnerabilities Catalog.
Vulnrichment	CC0 1.0 Universal	Source: CISA Vulnrichment.
GHSA	CC BY 4.0	Source: GitHub Advisory Database. Licensed under CC BY 4.0.
Sigma	Detection Rule License 1.1	Source: SigmaHQ. Licensed under DRL 1.1. Rule author attribution is preserved in triples.
ExploitDB	GPLv2+	Source: OffSec ExploitDB. Derived factual metadata (IDs, CVE mappings, dates) extracted under GPLv2+.
MISP Galaxies	CC0 1.0 / BSD 2-Clause	Source: MISP Project. Dual-licensed under CC0 1.0 and BSD 2-Clause.
LOLBAS	GPLv3	Source: LOLBAS Project. Licensed under GPLv3.
LOLDrivers	Apache 2.0	Source: LOLDrivers (magicsword.io).
Atomic Red Team	MIT License	Source: Red Canary Atomic Red Team. Licensed under MIT.
NIST 800-53 Mappings	Apache 2.0	© MITRE Engenuity, Center for Threat-Informed Defense.
Nuclei Templates	MIT License	Source: ProjectDiscovery Nuclei Templates. Licensed under MIT.
EUVD	Public (ENISA)	Source: ENISA European Vulnerability Database. Data published by the European Union Agency for Cybersecurity.
OSV	CC BY 4.0	Source: OSV (osv.dev). Licensed under CC BY 4.0.

License

Apache 2.0 — see Source Licensing & Attribution for individual source terms.

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