README.md

---
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<n<1M
---

# 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**](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)