CodeRecon - Deep Architectural Context Building

Build comprehensive architectural understanding through ultra-granular code analysis. Designed for security auditors, code reviewers, and developers who need to rapidly understand unfamiliar codebases before diving deep.

Overview

CodeRecon is a systematic approach to codebase reconnaissance that builds layered understanding from high-level architecture down to implementation details. Inspired by Trail of Bits' audit-context-building methodology.

Why CodeRecon?

Before you can find vulnerabilities, you need to understand:

How the system is architected
Where data flows
What the trust boundaries are
Where security-critical logic lives

This skill provides a structured methodology for building that context efficiently.

The Recon Pyramid

                    ┌─────────────┐
                    │   DETAILS   │  ← Implementation specifics
                   ─┼─────────────┼─
                  / │  FUNCTIONS  │  ← Key function analysis
                 /  ─┼─────────────┼─
                /   │   MODULES   │  ← Component relationships
               /    ─┼─────────────┼─
              /     │ ARCHITECTURE│  ← System structure
             /      ─┼─────────────┼─
            /       │   OVERVIEW  │  ← High-level understanding
           ─────────┴─────────────┴─────────

Start broad, go deep systematically.

Phase 1: Overview Reconnaissance

1.1 Project Identification

Gather basic project information:

# Check for documentation
ls -la README* ARCHITECTURE* SECURITY* CHANGELOG* docs/

# Identify build system
ls package.json Cargo.toml go.mod pyproject.toml Makefile

# Check for tests
ls -la test* spec* *_test* __tests__/

# Identify CI/CD
ls -la .github/workflows/ .gitlab-ci.yml Jenkinsfile .circleci/

1.2 Technology Stack Detection

# Language distribution
find . -type f -name "*.py" | wc -l
find . -type f -name "*.js" -o -name "*.ts" | wc -l
find . -type f -name "*.go" | wc -l
find . -type f -name "*.rs" | wc -l
find . -type f -name "*.sol" | wc -l

# Framework indicators
grep -r "from flask" --include="*.py" | head -1
grep -r "from django" --include="*.py" | head -1
grep -r "express\|fastify" --include="*.js" | head -1
grep -r "anchor_lang" --include="*.rs" | head -1

1.3 Dependency Analysis

# Python dependencies
cat requirements.txt pyproject.toml setup.py 2>/dev/null | grep -E "^\s*[a-zA-Z]"

# Node.js dependencies
cat package.json | jq '.dependencies, .devDependencies'

# Rust dependencies
cat Cargo.toml | grep -A 100 "\[dependencies\]"

# Go dependencies
cat go.mod | grep -E "^\s+[a-z]"

1.4 Create Technology Map

## Technology Map: [PROJECT NAME]

### Languages
| Language | Files | Lines | Primary Use |
|----------|-------|-------|-------------|
| Python | 150 | 25K | Backend API |
| TypeScript | 80 | 12K | Frontend |
| Solidity | 12 | 2K | Smart Contracts |

### Key Dependencies
| Package | Version | Purpose | Security Notes |
|---------|---------|---------|----------------|
| fastapi | 0.100.0 | Web framework | Recent CVEs: None |
| web3.py | 6.0.0 | Blockchain client | Check signing |
| pyjwt | 2.8.0 | JWT handling | Verify alg checks |

### Infrastructure
- Database: PostgreSQL 15
- Cache: Redis 7
- Message Queue: RabbitMQ
- Container: Docker + K8s

Phase 2: Architecture Mapping

2.1 Directory Structure Analysis

# Top-level structure
tree -L 2 -d

# Identify entry points
find . -name "main.py" -o -name "app.py" -o -name "index.ts" -o -name "main.go"

# Identify config
find . -name "config*" -o -name "settings*" -o -name ".env*"

2.2 Component Identification

Look for common patterns:

project/
├── api/           # HTTP endpoints
├── auth/          # Authentication
├── core/          # Business logic
├── db/            # Database layer
├── models/        # Data models
├── services/      # External services
├── utils/         # Utilities
├── workers/       # Background jobs
└── tests/         # Test suite

2.3 Create Architecture Diagram

┌─────────────────────────────────────────────────────────────┐
│                        CLIENTS                              │
│              (Web, Mobile, API Consumers)                   │
└─────────────────────────┬───────────────────────────────────┘
                          │ HTTPS
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                      API GATEWAY                            │
│                   (Rate Limiting, Auth)                     │
└─────────────────────────┬───────────────────────────────────┘
                          │
          ┌───────────────┼───────────────┐
          ▼               ▼               ▼
    ┌──────────┐   ┌──────────┐   ┌──────────┐
    │  Auth    │   │  Core    │   │  Admin   │
    │ Service  │   │  API     │   │  API     │
    └────┬─────┘   └────┬─────┘   └────┬─────┘
         │              │              │
         └──────────────┼──────────────┘
                        │
          ┌─────────────┼─────────────┐
          ▼             ▼             ▼
    ┌──────────┐  ┌──────────┐  ┌──────────┐
    │ Database │  │  Cache   │  │ External │
    │ (Postgres)│  │ (Redis)  │  │  APIs    │
    └──────────┘  └──────────┘  └──────────┘

2.4 Trust Boundary Identification

Map where trust levels change:

## Trust Boundaries

### Boundary 1: Internet → API Gateway
- **Type:** Network boundary
- **Controls:** TLS, Rate limiting, WAF
- **Risks:** DDoS, Injection, Auth bypass

### Boundary 2: API Gateway → Services
- **Type:** Authentication boundary
- **Controls:** JWT validation, Role checks
- **Risks:** Token forgery, Privilege escalation

### Boundary 3: Services → Database
- **Type:** Data access boundary
- **Controls:** Query parameterization, Connection pooling
- **Risks:** SQL injection, Data leakage

### Boundary 4: Services → External APIs
- **Type:** Third-party integration
- **Controls:** API keys, Request signing
- **Risks:** SSRF, Secret exposure

Phase 3: Module Deep Dive

3.1 Entry Point Analysis

For each entry point type:

# HTTP Routes - map all endpoints
grep -rn "@app.route\|@router\|@api_view" --include="*.py"
grep -rn "app.(get|post|put|delete)\|router.(get|post)" --include="*.ts"

# CLI Commands
grep -rn "@click.command\|argparse\|clap" --include="*.py" --include="*.rs"

# Event Handlers
grep -rn "@consumer\|@handler\|on_message" --include="*.py"

3.2 Create Entry Point Map

## Entry Points

### HTTP API
| Method | Path | Handler | Auth | Input |
|--------|------|---------|------|-------|
| POST | /api/login | auth.login | None | JSON body |
| GET | /api/users | users.list | JWT | Query params |
| POST | /api/transfer | tx.transfer | JWT + 2FA | JSON body |
| GET | /admin/logs | admin.logs | Admin JWT | Query params |

### WebSocket
| Event | Handler | Auth | Data |
|-------|---------|------|------|
| connect | ws.connect | JWT | None |
| message | ws.message | Session | JSON |

### Background Jobs
| Queue | Handler | Trigger | Data Source |
|-------|---------|---------|-------------|
| emails | email.send | API call | Database |
| reports | report.gen | Cron | Database |

3.3 Data Flow Tracing

For each critical endpoint, trace data flow:

POST /api/transfer
       │
       ▼
┌──────────────────┐
│ Request Parser   │ ← Validate JSON schema
│ (validation.py)  │
└────────┬─────────┘
         │ TransferRequest
         ▼
┌──────────────────┐
│ Auth Middleware  │ ← Verify JWT, extract user
│ (middleware.py)  │
└────────┬─────────┘
         │ User context
         ▼
┌──────────────────┐
│ Transfer Service │ ← Business logic
│ (transfer.py)    │
└────────┬─────────┘
         │
    ┌────┴────┐
    ▼         ▼
┌────────┐ ┌────────┐
│ DB     │ │External│
│ Write  │ │ API    │
└────────┘ └────────┘

zz-code-recon

Como adicionar

Cole no README do seu repo

Skills relacionadas

security-research

security-scanning-security-dependencies

vulnerability-scanner

dependency-management-deps-audit

Receba novas skills de Segurança toda segunda