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🛡️ Ghostwyre — Security Scanner Architecture Plan

Ghostwyre is a modular security scanning system combining a fast Go-based scanner engine with an AI-driven analysis layer for reasoning, prioritization, and reporting.

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PLAN

GhostWyre/
├── scanner/                          # Go scanning engine
│   ├── cmd/
│   │   ├── main.go                   # CLI entrypoint
│   │   └── scan.go                   # `ghostwyre scan` command
│   │
│   ├── internal/
│   │   ├── crawler/                  #   
│   │   ├── probe/                    # HTTP, TLS, DNS, security header checks
│   │   ├── injector/                 # XSS, SQLi, open redirect payloads
│   │   ├── fingerprint/              # Tech stack detection
│   │   └── reporter/                 # JSON/HTML report generation
│   │
│   └── pkg/
│       ├── config/                   # YAML + env configuration loader
│       └── httpclient/               # Shared HTTP client, retries, tracing
│
├── agent/                            # Python AI orchestration layer
│   ├── orchestrator.py               # LangGraph workflow
│   │
│   ├── tools/
│   │   ├── scan_tool.py              # Execute Go scanner
│   │   ├── vuln_lookup.py            # CVE/NVD enrichment
│   │   └── report_tool.py            # Findings summarization
│   │
│   └── prompts/
│       ├── planner.md
│       ├── analyst.md
│       ├── scorer.md
│       └── reporter.md
│
├── api/                              # Service layer
│   └── server.py                     # FastAPI endpoints
│
├── output/                           # Generated reports
│   ├── scans/
│   ├── reports/
│   └── exports/
│
├── configs/
│   ├── config.yaml
│   └── rules.yaml
│
├── docs/
│   ├── architecture.md
│   └── roadmap.md
│
├── Makefile
├── go.mod
├── go.sum
├── requirements.txt
└── README.md

🚀 System Overview

Ghostwyre is designed as a hybrid architecture:

• Go CLI Scanner → fast, deterministic, low-level scanning
• Python AI Orchestrator → reasoning, CVE mapping, risk scoring
• Web/API Layer → visualization, automation, scheduling

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🧱 Architecture Flow

User Prompt: "Scan example.com and tell me what's risky"
                 ↓
        LangGraph Orchestrator
        ├── Planner Agent
        │     → decides scan strategy
        ├── Scanner Tool (Go CLI)
        │     → returns structured JSON findings
        ├── Analyst Agent
        │     → interprets vulnerabilities + CVEs
        ├── Risk Scorer
        │     → assigns severity / CVSS-like score
        └── Reporter Agent
              → generates human-readable report

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🏗️ Phase 1 — Core Scanner Foundation (MVP)

Goal

Build a stable, fast scanning CLI with structured output.

Tasks

Component	Description
CLI Framework	Add cobra → ghostwyre scan --target https://example.com --depth 3
Config System	YAML config + environment variable overrides
Output Format	Replace logs with structured JSON findings
HTTP Probe Module	Extract headers, TLS cert info, CORS, CSP, HSTS
Basic Crawler	BFS-based link discovery with configurable depth

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⚔️ Phase 2 — Active Scanning Layer

Goal

Introduce security detection capabilities.

Tasks

Component	Description
Fingerprinting	Detect CMS, frameworks, server versions via headers + response body
Passive Vulnerability Checks	Match detected versions with known CVEs
Injection Probes	Basic XSS, SQL error-based detection, open redirects
Rate Limiting	Prevent target overload with configurable delays

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🧠 Phase 3 — AI Intelligence Layer

Goal

Add reasoning, prioritization, and explanation.

Flow

Go Scanner → JSON Output → AI Orchestrator → Risk Analysis → Report

Components

Agent	Responsibility
Planner Agent	Chooses scan modules based on user prompt
Scanner Tool	Executes Go binary and returns structured results
Analyst Agent	Interprets findings, maps CVEs
Risk Scorer	Assigns severity (CVSS-like scoring)
Reporter Agent	Generates human-readable security report

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Example Prompt Flow

User: "Scan example.com and tell me what's risky"

→ Planner selects modules
→ Go scanner executes crawl + probes
→ JSON findings returned
→ AI analyzes vulnerabilities
→ Final report generated

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🖥️ Phase 4 — Interface & Automation

Goal

Make Ghostwyre usable in real environments (UI + API + scheduling)

Features

• Web dashboard for scan history
• REST API for programmatic scanning
• Scheduled scans (cron / Temporal integration)
• Export reports (JSON → HTML/PDF)

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🧰 Technology Stack

Layer	Tech	Reason
CLI Engine	Go (cobra, viper)	Fast, production-ready CLI tooling
HTTP / Crawling	net/http or colly	Reliable web crawling support
AI Orchestration	LangGraph (Python)	Structured multi-agent workflows
LLM	Claude (Sonnet)	Strong reasoning + security analysis
API Layer	FastAPI	Async, simple integration
Output Format	JSON → HTML templates	Portable + diff-friendly reports

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📦 Output Design Principle

All scanner outputs must be:

• Structured (JSON)
• Machine-readable
• Agent-friendly
• Versionable
• Diff-trackable

Example:

{
  "target": "https://example.com",
  "findings": [
    {
      "type": "missing_header",
      "name": "CSP",
      "severity": "medium"
    }
  ]
}

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🎯 Design Philosophy

• Go handles speed + correctness
• Python handles reasoning + intelligence
• AI never directly scans — it interprets results
• Scanner remains deterministic and reproducible

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🔐 Future Enhancements

• Authenticated scanning modules
• Plugin system for custom checks
• Distributed scanning workers
• Real-time attack surface monitoring
• SIEM integrations (Splunk, ELK)

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