Cyber Risk Time Machine: Historical Intelligence & Replay Workbench

Interactive Time Machine Workbench

Select a target system below to explore its historical timeline. Navigate across key timestamps to uncover what Hermes knew, what threat signals were detected, and crucially what unknowns were formally acknowledged at that exact moment in time:

Cyber Risk Time Machine

Immutable historical replay & audit (« What did Hermes know at the time? »)

INVESTIGATION TARGET:

P5 Historical Immutability : Scores and inferences cryptographically sealed P3 Explicit Uncertainty : Recorded knowledge gaps preserved

2026-09-17 ACCELERATING

HERMES THREAT SCORE : 99/100

WHAT HERMES KNEW AT THAT PRECISE DATE

Loading state...

Factual state: ...

RECORDED UNKNOWNS & GAPS AT TIMESTAMP (P3)

...

Cryptographic Graph Seal: ...

SNAPSHOTS CATALOG

Every daily snapshot is etched to disk and served via static REST endpoints.

SNAP-2026-09-17 2026-09-17

512 entities • 110 software • 104 KEV

/api/snapshots/2026-09-17.json ↗

SNAP-2026-09-16 2026-09-16

494 entities • 108 software • 101 KEV

/api/snapshots/2026-09-16.json ↗

SNAP-2026-09-15 2026-09-15

480 entities • 106 software • 101 KEV

/api/snapshots/2026-09-15.json ↗

SNAP-2026-09-10 2026-09-10

450 entities • 102 software • 98 KEV

/api/snapshots/2026-09-10.json ↗

Permanent endpoint: /api/snapshots/latest.json ↗

1. The Need for Historical Cyber Risk Replay

Most vulnerability and threat databases suffer from hindsight bias and state volatility:

Once a vulnerability is added to the CISA Known Exploited Vulnerabilities (KEV) catalog, historical scores are often bumped upward retroactively or scrubbed clean.
Analysts conducting incident response or post-mortems cannot answer the essential question:

« What did our threat intelligence actually know three weeks before the breach occurred? »

The Hermes Cyber Risk Time Machine solves this epistemological dilemma by preserving an immutable, day-by-day record of intelligence assertions, machine observations, and explicit knowledge gaps.

2. Core Architectural Principles

P5 — Historical Immutability

Evaluations, predictions, and threat trajectories are immutable. If an early prediction underestimated a zero-day exploit, that underestimation remains etched into the record alongside the post-mortem analysis.

P3 — Explicit Uncertainty

Every historical record includes an explicit register of what Hermes did not know at that date (e.g., weaponization status, private zero-day telemetry, patch bypasses).

Cryptographic Integrity

Daily states are anchored by a SHA-256 hash of the complete Hermes Knowledge Graph, guaranteeing that snapshots have not been modified after the fact.

100% Static & Open REST API

Every historical snapshot is published as static JSON on our edge CDN, enabling friction-free programmatic verification without authentication or database latency.

3. Case Studies: Replaying Past Critical Accelerations

Case Study A: Cisco Identity Services Engine (CVE-2026-76460)

T-16 (2026-09-01): HTS 42 (STABLE). Bug tracked internally by vendor. Zero public telemetry.
T-9 (2026-09-08): HTS 58 (EMERGING). Cisco releases initial advisory. No public exploit code observed. Hermes explicitly registers unknown weaponization probability.
T-3 (2026-09-14): HTS 85 (ACCELERATING). GitHub token replay PoC detected. Exploit weaponization confirmed in lab conditions.
Day 0 (2026-09-17): HTS 99 (ACCELERATING). In-the-wild mass scanning triggers CISA KEV inclusion. Emergency patching required.

Case Study B: Adobe Commerce / Magento (CVE-2026-75650)

T-22 (2026-08-25): HTS 38 (STABLE). Routine release.
T-11 (2026-09-05): HTS 64 (EMERGING). Dark web chatter regarding CSS inlining Server-Side Template Injection (Filter.php).
T-4 (2026-09-12): HTS 88 (ACCELERATING). Honeypot detection of Rust backdoors deployed via compromised Magento checkout templates.
Day 0 (2026-09-16): HTS 99 (ACCELERATING). CISA KEV addition and global Magecart payment skimming campaign.

4. Programmatic API Access

You can inspect, diff, and audit Hermes historical intelligence programmatically via standard HTTP GET requests:

# Fetch the latest cryptographically sealed snapshot
curl -s https://codex.hermes-cyber.com/api/snapshots/latest.json | jq .

# Fetch the historical snapshot for September 17, 2026
curl -s https://codex.hermes-cyber.com/api/snapshots/2026-09-17.json | jq .

# Fetch complete historical trajectory datasets for high-profile targets
curl -s https://codex.hermes-cyber.com/api/snapshots/timelines.json | jq .

import urllib.request
import json

url = "https://codex.hermes-cyber.com/api/snapshots/index.json"
with urllib.request.urlopen(url) as response:
    catalog = json.loads(response.read().decode())

print(f"Total historical snapshots: {catalog['total_snapshots']}")
for snap in catalog['snapshots']:
    print(f"[{snap['date']}] Hash: {snap['graph_hash'][:16]}... Entities: {snap['entities']}")

Methodology: Historical Immutability (P5) Deep dive into the epistemic discipline preventing retrospective score adjustments and hindsight distortion.

Methodology: Threat Trajectory (HTR) Learn how Hermes computes velocity and acceleration to detect emerging threats before KEV listing.

Cyber Risk Time Machine: Historical Intelligence & Replay Workbench

Interactive Time Machine Workbench