Hermes Autopsy: Reverse Forensic & Counterfactual Workbench

Operational Post-Mortem Intelligence

Moving Beyond Narrative Storytelling: Traditional incident post-mortems narrate what happened chronologically, often obscuring the precise control failures and counterfactual interception opportunities. Hermes Autopsy reverses the forensic timeline from terminal impact (T₀) backwards to initial recon, classifies defensive breakdowns into a 4-tier failure taxonomy, and simulates defensive interventions with quantitative Interception Efficiency (E_c).

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Interactive Autopsy Workbench

Select an incident autopsy below to explore its reverse timeline, inspect defensive telemetry gaps, and simulate counterfactual interventions:

REVERSE FORENSIC AUTOPSY & COUNTERFACTUAL INTERCEPT MATRIX

HERMES METHODOLOGY 1.5

Dissect real-world cyber incidents starting from terminal blast impact (T₀) backwards to initial reconnaissance. Simulate counterfactual defensive interventions to compute interception efficiency and avoided blast radius.

AUTOPSY-2026-001 StyleSmuggler E-Commerce Magecart Infiltration CVE-2026-75650 AUTOPSY-2026-002 Cisco ISE Guest Portal Lateral Extrusion CVE-2026-76460 AUTOPSY-2026-003 Autonomous Agent Tool Execution Hijacking CVE-2026-27966

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1. What Hermes Autopsy Reveals

Post-mortems should not be static PDF reports buried in internal folders. They must produce reusable, structured intelligence:

Reverse Forensic Chronology

Starting at terminal blast impact (T₀) forces defenders to analyze the cascade backwards: How did data leave the perimeter? How was persistence established? How did unauthenticated traffic penetrate perimeter gates?

4-Tier Control Failure Taxonomy

Defensive controls do not simply “fail”. Hermes Autopsy classifies every breakdown into: BYPASSED (flawed capability vs exploit), MISCONFIGURED (human/operational omission), UNMONITORED (telemetry generated but never reviewed), or ABSENT (missing architectural control).

Counterfactual Interception Matrix

Evaluates alternative history: « If we had implemented strict egress domain whitelisting or container read-only roots, at what step would the kill chain have been severed? »

Blast-Radius Reduction Modeling

Computes quantitative blast-radius reduction (1 - E_comb), showing the direct systems and downtime saved by early-stage controls versus late-stage detection.

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2. Published Real-World Incident Autopsies

Incident 1: StyleSmuggler E-Commerce Magecart Infiltration (AUTOPSY-2026-001)

• Primary Flaw: Adobe Magento CSS inlining SSTI (CVE-2026-75650).
• Terminal Impact: 420,000 plaintext credit card transactions exfiltrated; 36h checkout platform downtime.
• Root Failure: WAF looked for classic PHP/SQLi syntax; blind to CSS @import template injection. Outbound egress firewall permitted uninspected HTTPS POST to bulletproof hosting.
• Optimal Counterfactual: Strict egress domain filtering (E_c = 88%) or read-only container root (E_c = 92%).

Incident 2: Cisco ISE Guest Portal Lateral Extrusion (AUTOPSY-2026-002)

• Primary Flaw: Cisco ISE token-replay authentication bypass (CVE-2026-76460).
• Terminal Impact: 45 substations affected; regional SCADA management telemetry disrupted.
• Root Failure: Guest portal token handler accepted forged cryptographic signatures. ISE service account possessed Domain Admin privileges.
• Optimal Counterfactual: AD Tiering separation (E_c = 95%) and strict guest segment isolation (E_c = 98%).

Incident 3: Autonomous Agent Tool Execution Hijacking (AUTOPSY-2026-003)

• Primary Flaw: Langflow unauthenticated REPL command execution (CVE-2026-27966).
• Terminal Impact: AWS production secrets extracted; 180,000 investor portfolio valuation tables dumped.
• Root Failure: Autonomous agent granted unconstrained bash execution without a JIT human-in-the-loop approval gate.
• Optimal Counterfactual: JIT human approval gate (E_c = 99%) and container sandboxing (E_c = 94%).

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3. Programmatic API Access

Every incident autopsy, reverse timeline step, and counterfactual matrix is published as static JSON on our edge CDN:

curl (Catalog of Autopsies)

# Fetch the complete index of published incident autopsies
curl -s https://codex.hermes-cyber.com/api/autopsy/index.json | jq .

curl (Specific Incident Autopsy)

# Fetch detailed reverse timeline and counterfactual matrix for Magecart incident
curl -s https://codex.hermes-cyber.com/api/autopsy/AUTOPSY-2026-001.json | jq .

Python (Counterfactual Calculator)

import urllib.request
import json

url = "https://codex.hermes-cyber.com/api/autopsy/AUTOPSY-2026-001.json"
with urllib.request.urlopen(url) as resp:
    autopsy = json.loads(resp.read().decode())

print(f"Incident: {autopsy['incident_name']}")
print(f"Actual Systems Compromised: {autopsy['blast_radius_actual']['systems_affected']}")
print("\nCounterfactual Defensive Interventions:")
for cf in autopsy["counterfactual_matrix"]:
    print(f"  [{cf['intervention_id']}] {cf['title']}: {cf['interception_efficiency']}% efficiency (Friction: {cf['friction_cost']})")

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4. Methodological Foundations

Methodology: Hermes Autopsy & Counterfactual Matrix Read the complete formal specification of reverse forensic timelines, 4-tier control failures, and counterfactual efficiency modeling.

Vulnerability Genome Sequencer Inspect the modular loci, root primitives, and phylogenetic lineages that underpin these incident vulnerabilities.