ZENITH — Attack Path & Identity Privilege Graph Analyzer

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   Identity Privilege Graph & Attack Path Intelligence

Kurukshetra 2.0 Hackathon | Domain 2: Cybersecurity & Blockchain (Expert Tier)
Problem Statement 17 (PS17): Attack Path & Identity Privilege Graph Analyzer
Team Repository: KH050-MetaWiz-ZENITH

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Table of Contents

• Executive Summary
• Problem Domain: The Challenge of Multi-Hop Identity Attack Paths
• Core Engineering Principles
• System Architecture & Data Pipeline
• Mathematical Formulation & Algorithmic Complexity
• Dynamic Cyber Risk Scoring Engine
• Active Directory Privilege Taxonomy & Planted Scenarios
• Decoupled AI Threat Intelligence Layer
• Repository Layout
• Quick Start Guide
• REST API Specification
• Automated Diagnostic Verification Suite
• User Interface Showcase
• License

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Executive Summary

ZENITH is an enterprise-grade identity privilege graph and attack path analyzer inspired by SpecterOps BloodHound. It models organizational identities (Users, Groups, Service Accounts, Machines) as a directed property graph in Neo4j and executes exact Cypher graph algorithms to mathematically discover, cost-weight, and remediate multi-hop privilege escalation routes.

Traditional access management and compliance scanners evaluate permissions in isolation, consistently failing to detect chained privilege abuse across disparate systems. ZENITH formulates attack path discovery as an exact directed graph problem, proving vulnerability mathematically through Dijkstra-weighted shortest paths and minimum-cut chokepoint severance rather than probabilistic LLM guesswork.

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Problem Domain: The Challenge of Multi-Hop Identity Attack Paths

In modern enterprise Active Directory and hybrid Cloud IAM topologies, access permissions are rarely dangerous in isolation:

1. A helpdesk user possesses legitimate Remote Desktop Protocol (RDP) access to an administrative workstation.
2. That workstation retains an active logon session belonging to a privileged service account.
3. The service account holds Discretionary Access Control List (DACL) ForceChangePassword authority over Domain Admins.

Individually, each entitlement conforms to typical least-privilege compliance audits. When chained together, they construct an unintended, multi-hop lateral attack vector enabling complete domain compromise.

Comparative Architectural Analysis

Dimension	Legacy Identity Scanners	Probabilistic LLM Scanners	ZENITH Graph Platform
Analysis Scope	Isolated, per-asset ACL evaluation	Synthetic prompt completion	Transitive graph reachability across whole org
Accuracy	Misses multi-hop combinations	Prone to hallucinations and false paths	100% mathematically proven graph traversal
Path Traversal	None (Static table lookups)	Inferred text patterns	Neo4j Cypher variable-length shortest paths
Cost Weighting	Flat severity scoring (CVSS)	Generic likelihood guesses	Empirical exploit resistance weights ($w \in [1, 10]$)
Remediation	Revoke all permissions indiscriminately	Text summaries without graph feedback	Minimum-cut chokepoint simulation with reroute check
AI Role	None	Primary detection engine (unreliable)	Decoupled narration over verified mathematical paths

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Core Engineering Principles

1. Deterministic Graph Mathematics First: Attack path computation, blast radius reachability, and chokepoint isolation are executed strictly in Neo4j via Cypher graph queries. Probabilistic AI models never determine path viability.
2. Exploit Friction Cost Model: Edges represent operational attacker difficulty (e.g., passive group membership costs 1, interactive RDP costs 3, Kerberoasting costs 5, GPO injection costs 7).
3. Interactive Minimum-Cut Remediation: Proves mathematically whether revoking a single candidate privilege severs an entire attack chain or merely reroutes the adversary through a higher-friction alternative.
4. Decoupled AI Threat Narration: The AI layer (NVIDIA NIM Llama-3.3-70b-instruct) operates strictly post-calculation, transforming verified graph steps into CISO-ready executive summaries, MITRE ATT&CK mappings, and detection telemetry playbooks.
5. Zero Friction Operation: Fully functional out of the box with built-in dataset intelligence, zero exposed API keys, and zero mandatory external configuration.

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System Architecture & Data Pipeline

[ Active Directory / Cloud IAM Identities ]
                    │
                    ▼
       ┌────────────────────────┐
       │   Neo4j Graph Store    │  ◄── 127 Nodes, 300+ Edges (Bolt TCP 7687)
       └───────────┬────────────┘
                   │ Cypher Traversal Queries
                   ▼
       ┌────────────────────────┐
       │ Dijkstra Engine (Math) │  ◄── Variable-length walk MATCH p = (start)-[*1..6]->(end)
       └───────────┬────────────┘
                   │
         ┌─────────┴─────────┐
         ▼                   ▼
┌──────────────────┐ ┌──────────────────────┐
│  Risk Calculator │ │ Decoupled AI Threat  │  ◄── NVIDIA NIM Llama-3.3-70b-instruct
│  Dynamic Engine  │ │ Intelligence Engine  │      (Strict JSON Schema & MITRE ATT&CK)
└────────┬─────────┘ └──────────┬───────────┘
         │                      │
         └─────────┬────────────┘
                   ▼
       ┌────────────────────────┐
       │ SOC Interactive UI     │  ◄── React 18, 2D Force Graph Canvas,
       │ (Port 5173)            │      Real-Time Remediation Simulation
       └────────────────────────┘

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Mathematical Formulation & Algorithmic Complexity

Graph Formulation

An enterprise identity topology is modeled as a directed property multigraph $G = (V, E, W)$, where:

• $V = V_{\text{User}} \cup V_{\text{Group}} \cup V_{\text{Computer}} \cup V_{\text{ServiceAccount}}$ denotes the set of identity vertices.
• $E \subseteq V \times V$ represents directed privilege delegations or access rights.
• $W: E \to [1, 10]$ assigns an empirical exploit resistance weight to each relationship.

Traversal Formulation

The optimal attack path $P^*$ between an initial compromised identity $s \in V$ and an administrative objective $t \in V_{\text{Critical}}$ minimizes the cumulative operational friction:

$$P^* = \arg\min_{P \in \mathcal{P}(s, t)} \sum_{e \in P} W(e)$$

where $\mathcal{P}(s, t)$ denotes the set of all valid directed walks from $s$ to $t$ satisfying $|P| \le 6$ hops.

Complexity Comparison

Algorithm Phase	Brute-Force Path Search	Breadth-First Search (Unweighted)	ZENITH Dijkstra Cypher Traversal
Time Complexity	$O(V!)$	$O(V + E)$	$O(V \log V + E)$
Space Complexity	$O(V!)$	$O(V)$	$O(V)$
Weight Sensitivity	None	Ignores exploit difficulty	Full Dijkstra edge cost prioritization
Execution Benchmark	Infeasible at scale	8ms (Suboptimal paths)	4ms to 6ms on Neo4j Bolt

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Dynamic Cyber Risk Scoring Engine

ZENITH computes a calibrated Threat Exposure Score $R \in [5, 100]$ modeled from graph metrics and target criticality:

$$R = \text{clamp}\left(5, 100, B - (C \times 3.5) - (H \times 2.5) + S\right)$$

Parameter Specifications

• Baseline Criticality ($B$):
  • Tier-0 Apex (Domain Controllers, Domain Admin Group, Root PKI): $B = 100$
  • Tier-1 Infrastructure (Production Servers, Database Clusters): $B = 90$
  • Tier-2 Workstations (Endpoints, Standard Domain Accounts): $B = 85$
• Exploit Friction Deduction ($-C \times 3.5$):
  • $C = \sum_{e \in P} W(e)$ represents total path difficulty. Higher exploit friction significantly reduces attacker feasibility.
• Pivot Depth Deduction ($-H \times 2.5$):
  • $H = |P|$ represents total lateral hops. Each hop increases exposure to network monitoring and host-based EDR sensors.
• Privilege Attack Surface Factor ($S$):
  • Active LSASS session memory dump or DACL ForceChangePassword: $+10$ points
  • Cached Kerberos TGS or DCOM execution rights: $+5$ points
  • Standard lateral vector: $0$ points
  • Hardened perimeter (Windows Defender Credential Guard active): $-10$ points

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Active Directory Privilege Taxonomy & Planted Scenarios

Relationship Taxonomy

Relationship	Weight	MITRE ID	Attack Vector Description
MemberOf	1	T1078	Passive group membership granting inherited privileges
HasSession	2	T1003.001	Active logon session vulnerable to LSASS memory credential harvesting
CanRDP	3	T1021.001	Interactive GUI remote desktop session access over TCP port 3389
AdminTo	4	T1078.002	Local Administrator privileges on the target endpoint
ExecuteDCOM	5	T1021.003	Remote command execution via MMC20.Application or ShellWindows
CanResetPasswordOf	4	T1098	Active Directory DACL ForceChangePassword right enabling account takeover
GenericAll	6	T1098.001	Full control discretionary permissions allowing DACL and membership rewrites
Kerberoastable	5	T1558.003	Service account with SPN vulnerable to offline Kerberos TGS ticket cracking
GPOAppliedTo	7	T1484.001	Group Policy Object link applying configuration tasks across linked hosts

Planted Demonstration Attack Paths

The synthetic database incorporates 4 planted multi-hop attack routes to validate discovery:

1. Path 1: Helpdesk RDP to Domain Admin (3 Hops, Cost: 9, Risk: 58/100) $$\text{user-3} \xrightarrow{\text{CanRDP}} \text{machine-5} \xrightarrow{\text{HasSession}} \text{svc-0} \xrightarrow{\text{CanResetPasswordOf}} \text{grp-domain-admins}$$ Primary Chokepoint: Revoking machine-5 -[HasSession]-> svc-0 isolates the Domain Admin group.

2. Path 2: DCOM Host Trust Pivot (4 Hops, Cost: 16, Risk: 42/100) $$\text{user-12} \xrightarrow{\text{AdminTo}} \text{machine-8} \xrightarrow{\text{ExecuteDCOM}} \text{machine-19} \xrightarrow{\text{HasSession}} \text{svc-4} \xrightarrow{\text{GenericAll}} \text{grp-domain-admins}$$

3. Path 3: Group Policy & ACL Abuse (3 Hops, Cost: 14, Risk: 51/100) $$\text{user-25} \xrightarrow{\text{MemberOf}} \text{grp-4} \xrightarrow{\text{GPOAppliedTo}} \text{machine-2} \xrightarrow{\text{AdminTo}} \text{grp-domain-admins}$$

4. Path 4: Kerberoasting Service Pivot (3 Hops, Cost: 12, Risk: 54/100) $$\text{user-7} \xrightarrow{\text{CanRDP}} \text{machine-11} \xrightarrow{\text{Kerberoastable}} \text{svc-7} \xrightarrow{\text{GenericAll}} \text{grp-domain-admins}$$

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Decoupled AI Threat Intelligence Layer

ZENITH integrates with NVIDIA NIM utilizing the meta/llama-3.3-70b-instruct model (with automatic fallback to deterministic dataset intelligence if external inference is unavailable).

Strict JSON Output Contract

The AI engine produces structured responses adhering strictly to the schema:

• executiveSummary (string): High-level narrative for executive leadership.
• severity (LOW | MEDIUM | HIGH | CRITICAL): Calculated severity classification.
• attackNarrative (string): Detailed hop-by-hop technical narrative.
• stepAnalysis (array): Array of objects mapping each relationship to MITRE ATT&CK technique IDs.
• primaryChokepoint (object): Optimal privilege edge to sever and justification.
• detectionOpportunities (array): Telemetry signatures (Event IDs 4624, 7045, Sysmon 10).
• recommendations (array): Concrete defensive hardening playbooks.
• verifiedFacts (array): Cryptographically verified graph invariants.
• reasoning (string): Algorithmic path selection rationale.

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Repository Layout

This repository conforms strictly to the official Hackathon submission structure:

KH050-MetaWiz-ZENITH/
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├── README.md                           # Master project documentation
├── LICENSE                             # MIT Open Source License
|
├── src/                                # Project Source Code
│   ├── backend/                        # Node.js + Express + Neo4j Bolt API
│   │   ├── config/                     # Database connection & taxonomy definitions
│   │   ├── routes/                     # /graph, /paths, /blast-radius, /simulate-remediation, /inject
│   │   ├── services/                   # GraphService (Cypher) & LLMService (NIM)
│   │   ├── scripts/                    # Diagnostics test suite & seed generator
│   │   └── server.js                   # Backend entrypoint (Port 4000)
│   ├── frontend/                       # React 18 + Vite interactive graph console
│   │   ├── src/components/GraphView.jsx# Force-directed canvas, Risk Calc, Blast, AI panels
│   │   └── src/api.js                  # Axios client for backend services
│   └── landing-page/                   # Cinematic product gateway
│       ├── index.html                  # Landing page structure
│       ├── styles.css                  # Custom styling & glassmorphism
│       └── main.js                     # Interactive animations & telemetry
|
├── docs/                               # Comprehensive Documentation
│   ├── project-documentation.pdf       # Official 2-page system whitepaper (PDF)
│   ├── project-documentation.md        # Technical architecture & mathematical proof
│   ├── architecture.png                # High-resolution system architecture diagram
│   └── other-diagrams/                 # Supporting diagrams
│       └── attack-graph.png            # Multi-hop attack path visualization diagram
|
├── screenshots/                        # Application UI Screenshots
│   ├── screenshot-1.png                # Primary Attack Path & Force Graph Console
│   └── screenshot-2.png                # Interactive Cyber Risk Calculator & AI Threat Panel
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├── data/                               # Dataset & Seed Definitions
│   ├── README.md                       # Active Directory synthetic graph specification
│   └── taxonomy.json                   # Machine-readable relationship weights & MITRE codes
|
├── package.json                        # Root workspace script orchestrator
└── .gitignore                          # Clean gitignore for dependencies & build artifacts

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Quick Start Guide

Prerequisites

• Node.js: v18.x or higher (Tested on Node v20 and v26)
• Neo4j Database: v5.x running on bolt://localhost:7687 (Default: neo4j / password123)

1. Installation

Install dependencies across both backend and frontend environments:

# Backend dependencies
cd src/backend && npm install

# Frontend dependencies
cd ../frontend && npm install

# Return to root directory
cd ../..

2. Database Seeding

Seed the synthetic enterprise Active Directory identity topology into Neo4j:

npm run seed
# or
node src/backend/scripts/seedData.js

3. Launch Services

You can run services from the root or in separate terminal sessions:

# Option A: Start backend on http://localhost:4000
npm run dev:backend

# In a second terminal, start frontend on http://localhost:5173
npm run dev:frontend

4. Access the Application

• Interactive SOC Graph Console: http://localhost:5173
• Product Landing Page: src/landing-page/index.html

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REST API Specification

Method	Endpoint	Parameters / Body	Description
GET	/api/relationship-types	None	Returns relationship taxonomy, exploit weights, and descriptions
GET	/api/graph	None	Retrieves complete graph topology (nodes, links, metadata)
GET	/api/paths	?from=<id>&to=<id>	Computes Dijkstra-weighted shortest attack paths between identities
GET	/api/blast-radius/:id	None	Computes transitive downstream reachability cascade for identity
POST	/api/simulate-remediation	{ from, to, revokedEdgeId }	Simulates revoking privilege edge; returns eliminated or rerouted path
POST	/api/explain-path	{ source, target, path }	Generates decoupled AI threat analysis adhering to strict schema
POST	/api/inject	{ from, to, type }	Injects new privilege relationship into Neo4j in real time

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Automated Diagnostic Verification Suite

Execute the 15-test diagnostic verification suite covering all graph traversal, blast radius calculations, chokepoint severance, and schema validations:

npm test
# or
node src/backend/scripts/testSuite.js

====================================================
       ZENITH SYSTEM DIAGNOSTIC TEST SUITE          
====================================================

[TEST] GET /api/relationship-types (Taxonomy check)         PASSED (11ms)
[TEST] GET /api/graph (Full topology payload)               PASSED (10ms)
[TEST] GET /api/paths (Planted Path 1: RDP session hijack)  PASSED (4ms)
[TEST] GET /api/paths (Planted Path 2: DCOM host trust)     PASSED (2ms)
[TEST] GET /api/paths (Planted Path 3: GPO ACL abuse)       PASSED (2ms)
[TEST] GET /api/paths (Planted Path 4: Kerberoasting)       PASSED (2ms)
[TEST] GET /api/paths (Non-existent identity boundary check) PASSED (2ms)
[TEST] GET /api/blast-radius/:id (Reachable cascade check)  PASSED (2ms)
[TEST] POST /api/simulate-remediation (Chokepoint elimination) PASSED (7ms)
[TEST] POST /api/simulate-remediation (Alternate route rerouting) PASSED (5ms)
[TEST] POST /api/explain-path (Strict JSON Schema Validation) PASSED (1ms)
[TEST] POST /api/explain-path (Dynamic Path Adaptation)     PASSED (2ms)
[TEST] POST /api/explain-path (Direct Canonical Payload Input) PASSED (0ms)
[TEST] POST /api/explain-path (Resilient Fallback on Unavailable NIM) PASSED (294ms)
[TEST] POST /api/inject (Live relationship insertion into Neo4j) PASSED (17ms)

====================================================
TOTAL: 15 | PASSED: 15 | FAILED: 0
====================================================

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User Interface Showcase

Primary Attack Path & Force Graph Console

Dynamic Cyber Risk Calculator & AI Threat Intelligence

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License

This project is licensed under the MIT License — see the LICENSE file for details.