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EPSS Integration Architecture

Advisory Source: docs/product-advisories/16-Dec-2025 - Merging EPSS v4 with CVSS v4 Frameworks.md
Last Updated: 2025-12-17
Status: Approved for Implementation

Executive Summary

EPSS (Exploit Prediction Scoring System) is a probabilistic model that estimates the likelihood a given CVE will be exploited in the wild over the next ~30 days. This document defines how StellaOps integrates EPSS as a first-class risk signal.

Key Distinction:

CVSS v4: Deterministic measurement of severity (0-10)
EPSS: Dynamic, data-driven probability of exploitation (0-1)

EPSS does not replace CVSS or VEX—it provides complementary probabilistic threat intelligence.

1. Design Principles

1.1 EPSS as Probabilistic Signal

Signal Type	Nature	Source
CVSS v4	Deterministic impact	NVD, vendor
EPSS	Probabilistic threat	FIRST daily feeds
VEX	Vendor intent	Vendor statements
Runtime context	Actual exposure	StellaOps scanner

Rule: EPSS modulates confidence, never asserts truth.

1.2 Architectural Constraints

Append-only time-series: Never overwrite historical EPSS data
Deterministic replay: Every scan stores the EPSS snapshot reference used
Idempotent ingestion: Safe to re-run for same date
Postgres as source of truth: Valkey is optional cache only
Air-gap compatible: Manual import via signed bundles

2. Data Model

2.1 Core Tables

Import Provenance

CREATE TABLE epss_import_runs (
  import_run_id      UUID PRIMARY KEY,
  model_date         DATE NOT NULL,
  source_uri         TEXT NOT NULL,
  retrieved_at       TIMESTAMPTZ NOT NULL,
  file_sha256        TEXT NOT NULL,
  decompressed_sha256 TEXT NULL,
  row_count          INT NOT NULL,
  model_version_tag  TEXT NULL,
  published_date     DATE NULL,
  status             TEXT NOT NULL,  -- SUCCEEDED / FAILED
  error              TEXT NULL,
  UNIQUE (model_date)
);

Time-Series Scores (Partitioned)

CREATE TABLE epss_scores (
  model_date    DATE NOT NULL,
  cve_id        TEXT NOT NULL,
  epss_score    DOUBLE PRECISION NOT NULL,
  percentile    DOUBLE PRECISION NOT NULL,
  import_run_id UUID NOT NULL REFERENCES epss_import_runs(import_run_id),
  PRIMARY KEY (model_date, cve_id)
) PARTITION BY RANGE (model_date);

Current Projection (Fast Lookup)

CREATE TABLE epss_current (
  cve_id        TEXT PRIMARY KEY,
  epss_score    DOUBLE PRECISION NOT NULL,
  percentile    DOUBLE PRECISION NOT NULL,
  model_date    DATE NOT NULL,
  import_run_id UUID NOT NULL
);

CREATE INDEX idx_epss_current_score_desc ON epss_current (epss_score DESC);
CREATE INDEX idx_epss_current_percentile_desc ON epss_current (percentile DESC);

Change Detection

CREATE TABLE epss_changes (
  model_date     DATE NOT NULL,
  cve_id         TEXT NOT NULL,
  old_score      DOUBLE PRECISION NULL,
  new_score      DOUBLE PRECISION NOT NULL,
  delta_score    DOUBLE PRECISION NULL,
  old_percentile DOUBLE PRECISION NULL,
  new_percentile DOUBLE PRECISION NOT NULL,
  flags          INT NOT NULL,  -- bitmask: NEW_SCORED, CROSSED_HIGH, BIG_JUMP
  PRIMARY KEY (model_date, cve_id)
) PARTITION BY RANGE (model_date);

2.2 Flags Bitmask

Flag	Value	Meaning
NEW_SCORED	0x01	CVE newly scored (not in previous day)
CROSSED_HIGH	0x02	Score crossed above high threshold
CROSSED_LOW	0x04	Score crossed below high threshold
BIG_JUMP_UP	0x08	Delta > 0.10 upward
BIG_JUMP_DOWN	0x10	Delta > 0.10 downward
TOP_PERCENTILE	0x20	Entered top 5%

3. Service Architecture

3.1 Component Responsibilities

┌─────────────────────────────────────────────────────────────────┐
│                    EPSS DATA FLOW                                │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌──────────────┐     ┌──────────────┐     ┌──────────────┐    │
│  │  Scheduler   │────►│  Concelier   │────►│   Scanner    │    │
│  │  (triggers)  │     │  (ingest)    │     │  (evidence)  │    │
│  └──────────────┘     └──────────────┘     └──────────────┘    │
│         │                    │                    │            │
│         │                    ▼                    │            │
│         │             ┌──────────────┐            │            │
│         │             │   Postgres   │◄───────────┘            │
│         │             │  (truth)     │                         │
│         │             └──────────────┘                         │
│         │                    │                                 │
│         ▼                    ▼                                 │
│  ┌──────────────┐     ┌──────────────┐                         │
│  │   Notify     │◄────│  Excititor   │                         │
│  │  (alerts)    │     │  (VEX tasks) │                         │
│  └──────────────┘     └──────────────┘                         │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Component	Responsibility
Scheduler	Triggers daily EPSS import job
Concelier	Downloads/imports EPSS, stores facts, computes delta, emits events
Scanner	Attaches EPSS-at-scan as immutable evidence, uses for scoring
Excititor	Creates VEX tasks when EPSS is high and VEX missing
Notify	Sends alerts on priority changes

3.2 Event Flow

Scheduler
  → epss.ingest(date)
    → Concelier (ingest)
      → epss.updated
        → Notify (optional daily summary)
      → Concelier (enrichment)
        → vuln.priority.changed
          → Notify (targeted alerts)
          → Excititor (VEX task creation)

4. Ingestion Pipeline

4.1 Data Source

FIRST publishes daily CSV snapshots at:

https://epss.empiricalsecurity.com/epss_scores-YYYY-MM-DD.csv.gz

Each file contains ~300k CVE records with:

cve - CVE ID
epss - Score (0.00000–1.00000)
percentile - Rank vs all CVEs

4.2 Ingestion Steps

Scheduler triggers daily job for date D
Download epss_scores-D.csv.gz
Decompress stream
Parse header comment for model version/date
Validate scores in [0,1], monotonic percentile
Bulk load into TEMP staging table
Transaction:
- Insert epss_import_runs
- Insert into epss_scores partition
- Compute epss_changes by comparing staging vs epss_current
- Upsert epss_current
- Enqueue epss.updated event
Commit

4.3 Air-Gap Import

Accept local bundle containing:

epss_scores-YYYY-MM-DD.csv.gz
manifest.json with sha256, source attribution, DSSE signature

Same pipeline, with source_uri = bundle://....

5. Enrichment Rules

5.1 New Scan Findings (Immutable)

Store EPSS "as-of" scan time:

public record ScanEpssEvidence
{
    public double EpssScoreAtScan { get; init; }
    public double EpssPercentileAtScan { get; init; }
    public DateOnly EpssModelDateAtScan { get; init; }
    public Guid EpssImportRunIdAtScan { get; init; }
}

This supports deterministic replay even if EPSS changes later.

5.2 Existing Findings (Live Triage)

Maintain mutable "current EPSS" on vulnerability instances:

scan_finding_evidence: Immutable EPSS-at-scan
vuln_instance_triage: Current EPSS + band (for live triage)

5.3 Efficient Delta Targeting

On epss.updated(D):

Read epss_changes where flags indicate material change
Find impacted vulnerability instances by CVE
Update only those instances
Emit vuln.priority.changed only if band crossed

6. Notification Policy

6.1 Default Thresholds

Threshold	Default	Description
HighPercentile	0.95	Top 5% of all CVEs
HighScore	0.50	50% exploitation probability
BigJumpDelta	0.10	Meaningful daily change

6.2 Trigger Conditions

Newly scored CVE in inventory AND percentile >= HighPercentile
Existing CVE crosses above HighPercentile or HighScore
Delta > BigJumpDelta AND CVE in runtime-exposed assets

All thresholds are org-configurable.

7. Trust Lattice Integration

7.1 Scoring Rule Example

IF cvss_base >= 8.0
AND epss_score >= 0.35
AND runtime_exposed = true
→ priority = IMMEDIATE_ATTENTION

7.2 Score Weights

Factor	Default Weight	Range
CVSS	0.25	0.0-1.0
EPSS	0.25	0.0-1.0
Reachability	0.25	0.0-1.0
Freshness	0.15	0.0-1.0
Frequency	0.10	0.0-1.0

8. API Surface

8.1 Internal API Endpoints

Endpoint	Description
`GET /epss/current?cve=...`	Bulk lookup current EPSS
`GET /epss/history?cve=...&days=180`	Historical time-series
`GET /epss/top?order=epss&limit=100`	Top CVEs by score
`GET /epss/changes?date=...`	Daily change report

8.2 UI Requirements

For each vulnerability instance:

EPSS score + percentile
Model date
Trend delta vs previous scan date
Filter chips: "High EPSS", "Rising EPSS", "High CVSS + High EPSS"
Evidence panel showing EPSS-at-scan vs current EPSS

9. Implementation Checklist

Phase 1: Data Foundation

DB migrations: tables + partitions + indexes
Concelier ingestion job: online download + bundle import

Phase 2: Integration

epss_current + epss_changes projection
Scanner.WebService: attach EPSS-at-scan evidence
Bulk lookup API (/api/v1/epss/*)

Phase 3: Enrichment

Scanner Worker EpssEnrichmentJob: update vuln_instance_triage for CVEs with material changes
Scanner Worker EpssSignalJob: generate tenant-scoped EPSS signals (stored in epss_signal; published via IEpssSignalPublisher when configured)

Phase 4: UI/UX

EPSS fields in vulnerability detail
Filters and sort by exploit likelihood
Trend visualization

Phase 5: Operations

Backfill tool (last 180 days)
Ops runbook: schedules, manual re-run, air-gap import

10. Operations Runbook

10.1 Configuration

EPSS jobs are configured via the Epss:* sections in Scanner Worker configuration:

Epss:
  Ingest:
    Enabled: true                    # Enable/disable the job
    Schedule: "0 5 0 * * *"         # Cron expression (default: 00:05 UTC daily)
    SourceType: "online"            # "online" or "bundle"
    BundlePath: null                # Path for air-gapped bundle import
    InitialDelay: "00:00:30"        # Wait before first run (30s)
    RetryDelay: "00:05:00"          # Delay between retries (5m)
    MaxRetries: 3                   # Maximum retry attempts
  Enrichment:
    Enabled: true                    # Enable/disable live triage enrichment
    PostIngestDelay: "00:01:00"     # Wait after ingest before enriching
    BatchSize: 1000                 # CVEs per batch
    HighPercentile: 0.99            # ≥ threshold => HIGH (and CrossedHigh flag)
    HighScore: 0.50                 # ≥ threshold => high score threshold
    BigJumpDelta: 0.10              # ≥ threshold => BIG_JUMP flag
    CriticalPercentile: 0.995       # ≥ threshold => CRITICAL
    MediumPercentile: 0.90          # ≥ threshold => MEDIUM
    FlagsToProcess: "NewScored,CrossedHigh,BigJumpUp,BigJumpDown" # Empty => process all
  Signal:
    Enabled: true                    # Enable/disable tenant-scoped signal generation
    PostEnrichmentDelay: "00:00:30" # Wait after enrichment before emitting signals
    BatchSize: 500                  # Signals per batch
    RetentionDays: 90               # Retention for epss_signal layer
    SuppressSignalsOnModelChange: true # Suppress per-CVE signals on model version changes

10.2 Online Mode (Connected)

The job automatically fetches EPSS data from FIRST.org at the scheduled time:

Downloads https://epss.empiricalsecurity.com/epss_scores-YYYY-MM-DD.csv.gz
Validates SHA256 hash
Parses CSV and bulk inserts to epss_scores
Computes delta against epss_current
Updates epss_current projection
Publishes epss.updated event

10.3 Air-Gap Mode (Bundle)

For offline deployments:

Download EPSS CSV from FIRST.org on an internet-connected system
Copy to the configured BundlePath location
Set SourceType: "bundle" in configuration
The job will read from the local file instead of fetching online

10.4 Manual Ingestion

There is currently no HTTP endpoint for one-shot ingestion. To force a run:

Temporarily set Epss:Ingest:Schedule to 0 * * * * * and Epss:Ingest:InitialDelay to 00:00:00
Restart Scanner Worker and wait for one ingest cycle
Restore the normal schedule

Note: a successful ingest triggers EpssEnrichmentJob, which then triggers EpssSignalJob.

10.5 Troubleshooting

Symptom	Likely Cause	Resolution
Job not running	`Enabled: false`	Set `Enabled: true`
Download fails	Network/firewall	Check HTTPS egress to `epss.empiricalsecurity.com`
Parse errors	Corrupted file	Re-download, check SHA256
Enrichment/signals not running	Storage disabled or job disabled	Ensure `ScannerStorage:Postgres:ConnectionString` is set and `Epss:Enrichment:Enabled` / `Epss:Signal:Enabled` are `true`
Slow ingestion	Large dataset / constrained IO	Expect <120s for ~310k rows; confirm via the perf harness and compare against CI baseline
Duplicate runs	Idempotent	Safe - existing data preserved

10.6 Monitoring

Key metrics and traces:

Activities
- StellaOps.Scanner.EpssIngest (epss.ingest): epss.model_date, epss.row_count, epss.cve_count, epss.duration_ms
- StellaOps.Scanner.EpssEnrichment (epss.enrich): epss.model_date, epss.changed_cve_count, epss.updated_count, epss.band_change_count, epss.duration_ms
- StellaOps.Scanner.EpssSignal (epss.signal.generate): epss.model_date, epss.change_count, epss.signal_count, epss.filtered_count, epss.tenant_count, epss.duration_ms
Metrics
- epss_enrichment_runs_total{result} / epss_enrichment_duration_ms / epss_enrichment_updated_total / epss_enrichment_band_changes_total
- epss_signal_runs_total{result} / epss_signal_duration_ms / epss_signals_emitted_total{event_type, tenant_id}
Logs (structured)
- EPSS ingest/enrichment/signal job started
- EPSS ingestion completed: modelDate={ModelDate}, rows={RowCount}, ...
- EPSS enrichment completed: updated={Updated}, bandChanges={BandChanges}, ...
- EPSS model version changed: {OldVersion} -> {NewVersion}
- EPSS signal generation completed: signals={SignalCount}, changes={ChangeCount}, ...

10.7 Performance

Local harness: src/Scanner/__Benchmarks/StellaOps.Scanner.Storage.Epss.Perf/README.md
CI workflow: .gitea/workflows/epss-ingest-perf.yml (nightly + manual, artifacts retained 90 days)

11. Anti-Patterns to Avoid

Anti-Pattern	Why It's Wrong
Storing only latest EPSS	Breaks auditability and replay
Mixing EPSS into CVE table	EPSS is signal, not vulnerability data
Treating EPSS as severity	EPSS is probability, not impact
Alerting on every daily fluctuation	Creates alert fatigue
Recomputing EPSS internally	Use FIRST's authoritative data

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