sarvekshanam_v2

Sarvekshanam

Advanced Multi-System Security Operations & AI Analysis Platform

Sarvekshanam (v2 Beta) is a distributed vulnerability orchestration and AI-assisted security operations platform built around a centralized Node.js Master and scalable Go-based Remote Runners.

Instead of acting as a single monolithic scanner, Sarvekshanam coordinates an entire fleet of execution nodes capable of running custom offensive security modules, collecting telemetry, streaming results in real time, and feeding findings into an agentic AI workflow powered by Retrieval-Augmented Generation (RAG).

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🧠 Architecture

The architecture is designed around four tightly integrated layers:

• 🖥️ Control Layer (Master Server) — Handles orchestration, authentication, module scheduling, fleet management, report aggregation, and AI coordination.
• ⚙️ Execution Layer (Remote Runners) — Lightweight Go agents that securely execute tasks inside ephemeral sandboxes across distributed infrastructure.
• 🧠 Intelligence Layer (AI + RAG) — Processes massive scan outputs, enables contextual AI conversations, summarizes findings, and assists in remediation workflows.
• 📡 Module Ecosystem — Supports dynamically hot-loaded Python, Bash, Node.js, and Go security modules without requiring recompilation.

This allows Sarvekshanam to function both as a traditional vulnerability orchestration platform and as an intelligent security operations environment capable of scaling from local testing labs to distributed enterprise fleets.

Note: This is Version 2 (currently in Beta) of the original Sarvekshanam project. V2 introduces distributed Go Slaves, RAG-based intelligence, and an Agentic AI assistant while preserving the orchestration-first philosophy of the original platform.

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✨ Features

• 🚀 Fleet Orchestration
  Manage distributed Go Slaves capable of executing arbitrary security scripts and workflows.

• 🤖 Agentic AI Operations
  Chat with scan results, generate summaries, correlate findings, and launch additional modules directly from the AI interface.

• 🛡️ Ephemeral Execution Sandboxes
  Every task executes inside isolated temporary environments to prevent cross-contamination between scans.

• 📦 Multi-Language Module Support
  Dynamically hot-load Python, Bash, Go, or Node.js modules simply by dropping them into the modules directory.

• 🔒 Enterprise-Grade Security
  RSA-OAEP encrypted payloads, JWKS authentication, JWT validation, and OIDC SSO integration.

• 🚄 Distributed Bulk Operations
  Execute security tooling across hundreds of targets simultaneously through remote execution nodes.

• 📚 AI-Powered Context Retention
  Massive outputs are indexed into vector memory using sqlite-vec for intelligent retrieval and contextual conversations.

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📸 Screenshots

Power User Dashboard	AI Chat Interface

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📚 Documentation

The full documentation is available in the docs/ directory and can be hosted via GitHub Pages.

Guide	Description
Architecture Overview	Internal system design and data flow
Getting Started	Installation and initial setup
Configuration Guide	Environment variables and runtime configuration
Module Development Guide	Building custom security modules
Security & Fleet Management	Authentication, encryption, and remote node management
AI & Context Integration	RAG pipeline and AI orchestration

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⚡ Quick Start

1. Requirements

• Node.js (v18+)
• Go (v1.25.5+) (optional for running local remote runners)

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2. Setup the Master Server

# Clone the repository
git clone https://github.com/A-Y-U-S-H-Y-A/sarveskshanam_v2.git

# Enter backend directory
cd sarveskshanam_v2/backend

# Install dependencies
npm install

# Configure environment
cp .env.example .env

Edit .env and configure:

• LLM API keys
• JWT secrets
• OIDC settings (optional)
• Fleet authentication configuration

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3. Start the Platform

npm run start

The platform will now be available at:

http://localhost:3000

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4. Post-Installation: Create an Admin

To manage remote runners, you need an administrative account.

1. Open http://localhost:3000 in your browser.
2. Register a new user account (e.g., alice).
3. In your terminal, inside the backend directory, promote the account:

node scripts/makeAdmin.js alice

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5. Configure Remote Runners (Optional)

Sarvekshanam is built to support distributed execution through lightweight remote runners.

The included Go-based runner can be deployed across VPS instances, internal infrastructure, or isolated lab systems to create a scalable security execution fleet.

See the Remote Runner README for setup instructions.

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🧩 Tech Stack

Layer	Technology
Frontend	Vanilla JS, Pure HTML/CSS, Monospace Minimal UI
Backend	Node.js, Express, Sequelize ORM
Database	SQLite, sqlite-vec
Authentication	Passport.js, JWKS, RSA Cryptography
AI Stack	LangChain, Ollama, OpenAI, Anthropic, Gemini, Groq
Remote Execution	Go 1.25, SSE Streaming
Vector Memory	Retrieval-Augmented Generation (RAG)

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📜 License

This project is licensed under the GNU General Public License v3.0 (GPLv3).

See the LICENSE file for more details.

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