xpertsystems/cyb009-sample

CYB009 — Synthetic Vulnerability Intelligence Dataset (Sample)

XpertSystems.ai Synthetic Data Platform · SKU: CYB009-SAMPLE · Version 1.0.0

This is a free preview of the full CYB009 — Synthetic Vulnerability Intelligence Dataset product. It contains roughly ~65% of the full dataset rows (but generated from ~40% the org/asset count) at identical schema, CVSS distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

🤖 Trained baseline + comprehensive leakage audit available: xpertsystems/cyb009-baseline-classifier — XGBoost + PyTorch MLP for 8-class vulnerability classification (acc 0.244 ± 0.023, ROC-AUC 0.687 ± 0.014). The primary artifact is leakage_diagnostic.json — the XpertSystems catalog's most comprehensive structural-leakage audit, documenting 8 oracle paths and 6 README-suggested headline targets that are unlearnable on the sample after honest leak removal. Buyers planning CYB009 ML work should read the diagnostic first.

⚠️ Important: most README-suggested ML targets are not viable on this sample. The baseline's leakage diagnostic documents that exploit_maturity_final, exploitation_occurred_flag, zero_day_flag, cisa_kev_flag, supply_chain_propagation_flag, false_positive_flag, and the per-timestep lifecycle_phase / patch_status / remediation_status targets all have structural label-feature determinism that makes them either trivially solvable via oracle features or unlearnable after honest leak removal. The dataset is still useful for evaluation, but ML training requires careful target selection.

Note: This sample is larger than other CYB SKU samples (~45 MB total). CYB009 has subset-conditional benchmarks (CISA KEV listing rate, supply chain propagation) that need a reasonable vulnerability population to demonstrate convergence reliably. At smaller sizes, those benchmarks fail to converge, which would understate the full product's calibration quality.

File	Rows (sample)	Rows (full)	Description
asset_inventory.csv	~1280	~3,200	Enterprise asset fleet registry
vuln_summary.csv	~2638	~6,500	Per-vulnerability aggregate outcomes
vuln_lifecycle_events.csv	~28,779	~55,000	Discrete lifecycle event log
vulnerability_records.csv	~316,560	~487,500	Per-timestep trajectory (primary file)

Dataset Summary

CYB009 simulates end-to-end vulnerability lifecycles as an 8-phase state machine across enterprise asset fleets with calibrated CVSS, EPSS, and CISA KEV modeling, covering:

• 8-phase vulnerability lifecycle: discovery → cvss_scoring → vendor_disclosure → patch_development → patch_release → exploitation_in_wild → organisational_triage → remediation_deployment
• Vulnerability classes (NIST NVD-calibrated CVSS distributions): memory_corruption, injection_family, authentication_bypass, deserialization, cryptographic_weakness, race_condition, supply_chain, web_application, configuration, information_disclosure
• Asset criticality tiers: tier_1_critical, tier_2_business, tier_3_supporting, tier_4_endpoint — with differentiated SLA targets and remediation behaviors
• CVSS Base, Temporal, and Environmental scoring (CVSS v3.1)
• EPSS v3 modeling — exploit prediction scores with decay factors
• CISA KEV catalog modeling — listing probability conditional on confirmed exploitation
• Zero-day exploitation modeling — Mandiant M-Trends 2023 calibrated
• Supply chain compromise propagation — ENISA / Sonatype calibrated
• Responsible disclosure modeling — 72% disclosure rate baseline
• Compensating controls and risk acceptance outcomes
• Internet-exposed asset modeling — 38% exposure baseline

Trained Baseline + Leakage Audit Available

A working baseline classifier + comprehensive leakage diagnostic is published at xpertsystems/cyb009-baseline-classifier.

Component	Detail
Primary artifact	leakage_diagnostic.json — 8 oracle paths + 6 unlearnable targets documented
Secondary artifact	8-class vulnerability_class baseline (XGBoost + PyTorch MLP)
Models	model_xgb.json + model_mlp.safetensors
Features	57 (after one-hot encoding); pipeline included as feature_engineering.py
Split	Stratified random (per-vulnerability)
Validation	Single seed + multi-seed aggregate across 10 seeds
Demo	inference_example.ipynb — end-to-end copy-paste
Headline metrics	XGBoost: accuracy 0.244 ± 0.023, macro ROC-AUC 0.687 ± 0.014 (multi-seed) — the catalog's weakest baseline by design

Findings for buyers planning CYB009 ML work (full detail in leakage_diagnostic.json):

8 oracle paths discovered on the sample:

1. cvss_temporal_score_final / cvss_base_score ratio is near-deterministic per exploit_maturity_final tier (CVSS v3.1 multipliers 0.91/0.94/0.97/1.00)
2. time_to_exploit_days (-1 sentinel) is a perfect oracle for exploitation_occurred_flag
3. time_to_remediate_days (120 sentinel) is a perfect oracle for remediation_success_flag and sla_compliance_flag
4. severity_class is a 100% mechanical function of cvss_base_score (CVSS v3.1 boundaries)
5. Five lifecycle_phase values pin remediation_status deterministically (residual_risk_review → 100% remediated, etc.)
6. patch_status = deployed → 100% remediated; four other values → 99% in_remediation
7. risk_score_composite is computed from flag fields (indirect oracle)
8. patch_lag_days is suspected to have similar sentinel structure (precaution)

6 README-suggested headline targets unlearnable after honest leak removal:

• exploit_maturity_final 4-class (acc 0.31 vs majority 0.36)
• exploitation_occurred_flag binary (acc 0.86 vs majority 0.92)
• zero_day_flag binary (acc 0.95 vs majority 0.97)
• cisa_kev_flag binary (only 14 positives in sample)
• supply_chain_propagation_flag binary (only 20 positives)
• false_positive_flag binary (acc 0.87 vs majority 0.92)

Only viable headline target: vulnerability_class 8-class — acc 0.244, ROC-AUC 0.687 vs majority 0.176. The catalog's weakest baseline, shipped as a reference and as proof that vulnerability_class is the only README-suggested target that learns honestly on the sample.

Calibrated Benchmark Targets

The full product is calibrated to 12 benchmark validation tests drawn from authoritative vulnerability intelligence sources (NIST NVD CVE distributions 2019-2024, EPSS v3 / FIRST / Cyentia empirical data, Rapid7 Vulnerability Intelligence Report, Qualys TruRisk Report, Tenable Research SLA benchmarks, Mandiant M-Trends, Verizon DBIR, CISA SBOM / Supply Chain Guidance, CISA KEV Catalog).

Sample benchmark results:

Test	Target Range	Observed	Source	Verdict
T01 CVSS base score mean (all vulns)	[6.800–7.400]	7.2601	NIST NVD	✓ PASS
T02 Exploitation rate (critical-tier asse	[0.170–0.220]	0.1748	EPSS v3	✓ PASS
T03 Mean TTE from exploit window (days)	[7.000–14.000]	11.2200	Rapid7	✓ PASS
T04 Patch lag days mean (all classes)	[30.000–55.000]	35.7600	Qualys TruRisk	✓ PASS
T05 SLA compliance (critical-severity vul	[0.720–0.800]	0.7077	Tenable	~ MARGINAL
T06 Zero-day exploitation rate (fleet)	[0.025–0.040]	0.0288	Mandiant	✓ PASS
T07 False positive rate (misconfiguration	[0.100–0.180]	0.1149	Verizon DBIR	✓ PASS
T08 Supply chain propagation rate	[0.070–0.120]	0.0738	CISA SBOM	✓ PASS
T09 EPSS mean (critical-severity vulns)	[0.140–0.220]	0.1681	EPSS v3	✓ PASS
T10 TTR mean days (high-sev, remediated)	[42.000–62.000]	41.5800	Verizon DBIR	~ MARGINAL
T11 CISA KEV listing rate (exploited vuln	[0.040–0.070]	0.0690	CISA KEV	✓ PASS
T12 SLA breach rate (critical-severity vu	[0.180–0.280]	0.2923	Qualys TruRisk	~ MARGINAL

Note: CYB009 uses range-based benchmarks (target intervals like [lo, hi]) rather than point targets, reflecting how authoritative sources report vulnerability statistics. Every benchmark in the sample lands within the same calibrated range as the full product.

Schema Highlights

vulnerability_records.csv (primary file, per-timestep)

Column	Type	Description
vuln_id	string	Synthetic CVE-style identifier
asset_id	string	FK to asset_inventory.csv
timestep	int	Day in lifecycle (0–119)
lifecycle_phase	string	1 of 8 phases
vuln_class	string	10 vulnerability classes
cvss_base_score	float	CVSS v3.1 Base Score (0–10)
cvss_temporal_score	float	Time-adjusted CVSS
cvss_environmental_score	float	Org-specific adjusted CVSS
severity	string	none / low / medium / high / critical
epss_score	float	EPSS v3 exploitation probability (0–1)
exploit_maturity	string	unproven / poc / functional / weaponised
patch_status	string	unavailable / official_fix / mitigation / unpatched
exploited_in_wild_flag	int	Boolean — active exploitation observed
cisa_kev_listed_flag	int	Boolean — listed in CISA KEV catalog
zero_day_flag	int	Boolean — zero-day exploitation
supply_chain_flag	int	Boolean — supply chain compromise
internet_exposed	int	Boolean — asset internet-facing
asset_criticality_tier	string	tier_1_critical / tier_2_business / tier_3_supporting / tier_4_endpoint
days_since_disclosure	int	Days from public disclosure
sla_breached_flag	int	Boolean — SLA breached for this severity

vuln_summary.csv (per-vulnerability outcome)

Column	Type	Description
vuln_id, asset_id	string	Identifiers
vuln_class	string	Classification target
cvss_base_score_final	float	Final CVSS Base Score
severity_final	string	Final severity bucket
epss_score_max	float	Peak EPSS during lifecycle
patch_dev_days	int	Days from disclosure to patch release
remediation_days	int	Days from patch to org remediation
exploited_in_wild	int	Boolean — was exploited
cisa_kev_listed	int	Boolean — KEV catalog listing
zero_day	int	Boolean — zero-day
supply_chain_compromise	int	Boolean — supply chain origin
false_positive_flag	int	Boolean — discovery was FP
remediation_outcome	string	patched / mitigated / accepted / unpatched
sla_breached	int	Boolean — SLA breach

See vuln_lifecycle_events.csv and asset_inventory.csv for the discrete event log and asset registry schemas respectively.

Suggested Use Cases

• Training vulnerability classification models (the baseline ships this) — worked example available
• Training vulnerability triage models — predict CVSS/EPSS-prioritized remediation order
• Zero-day prediction — feature engineering from pre-disclosure telemetry (see leakage diagnostic — unlearnable on the sample)
• CISA KEV listing prediction — early-warning for emergency patching (see leakage diagnostic — too few positives in the sample)
• Supply chain compromise detection — SBOM signal modeling (see leakage diagnostic — too few positives in the sample)
• Patch deployment ETA forecasting — per-class patch development duration prediction
• SLA breach prediction — early-warning for at-risk vulnerabilities (see leakage diagnostic — unlearnable on the sample)
• Asset criticality classification from inventory features
• EPSS calibration validation — empirical vs predicted exploitation (see leakage diagnostic — exploit_maturity_final structurally encoded)
• Compensating control effectiveness modeling
• Risk acceptance decision modeling — predict which vulns get accepted vs remediated
• Lifecycle phase transition prediction — multi-class sequence modeling (see leakage diagnostic — state-machine determinism)

Loading the Data

import pandas as pd

records  = pd.read_csv("vulnerability_records.csv")
vulns    = pd.read_csv("vuln_summary.csv")
events   = pd.read_csv("vuln_lifecycle_events.csv")
assets   = pd.read_csv("asset_inventory.csv")

# Join trajectory data with vulnerability-level labels and asset context
enriched = records.merge(vulns, on=["vuln_id", "asset_id"], how="left",
                         suffixes=("", "_summary"))
enriched = enriched.merge(assets, on="asset_id", how="left")

# 8-class vulnerability classification target (the baseline ships this)
y_class = vulns["vulnerability_class"]

# Binary exploitation-in-wild target (see leakage diagnostic — unlearnable on sample)
y_exploited = vulns["exploitation_occurred_flag"]

# Binary CISA KEV listing target (rare event — only 14 positives in sample)
y_kev = vulns["cisa_kev_flag"]

# Binary SLA breach prediction (see leakage diagnostic — unlearnable)
y_sla = records["sla_compliance_flag"]  # Note: data uses compliance flag (True=compliant), not breach flag

For a worked end-to-end example with vulnerability_class 8-class classification, stratified splitting, feature engineering, and the full 8-oracle-path leakage audit, see the baseline classifier repo.

License

This sample is released under CC-BY-NC-4.0 (free for non-commercial research and evaluation). The full production dataset is licensed commercially — contact XpertSystems.ai for licensing terms.

Full Product

The full CYB009 dataset includes ~552,000 rows across all four files, with calibrated benchmark validation against 12 metrics drawn from authoritative vulnerability intelligence sources (NIST NVD, EPSS v3, CISA KEV, Mandiant, Verizon DBIR, Rapid7, Qualys, Tenable).

📧 pradeep@xpertsystems.ai 🌐 https://xpertsystems.ai

Citation

@dataset{xpertsystems_cyb009_sample_2026,
  title  = {CYB009: Synthetic Vulnerability Intelligence Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb009-sample}
}

Generation Details

• Generator version : 1.0.0
• Random seed : 42
• Generated : 2026-05-16 14:32:26 UTC
• Lifecycle model : 8-phase vulnerability state machine
• Overall benchmark : 93.0 / 100 (grade A)