--- license: cc-by-nc-4.0 task_categories: - tabular-classification - time-series-forecasting tags: - cybersecurity - vulnerability-management - cve - cvss - epss - cisa-kev - synthetic-data - patch-management - supply-chain-security - zero-day pretty_name: CYB009 — Synthetic Vulnerability Intelligence (Sample) size_categories: - 100K 🤖 **Trained baseline + comprehensive leakage audit available:** > [**xpertsystems/cyb009-baseline-classifier**](https://huggingface.co/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](https://huggingface.co/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`](https://huggingface.co/xpertsystems/cyb009-baseline-classifier/blob/main/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](https://huggingface.co/xpertsystems/cyb009-baseline-classifier) - 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 ```python 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](https://huggingface.co/xpertsystems/cyb009-baseline-classifier). ## 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 ```bibtex @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)