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Zenvra Development Session — Complete

✅ Completed Tasks

1. Repository Exploration

  • Mapped codebase structure: CLI, Server (Rust/Axum), Web (SvelteKit 5), VS Code extension
  • Identified AI provider system (Anthropic, OpenAI, Google, Groq, custom)
  • Located existing CVE sync, secrets detection, and SAST engines

2. CLI Testing with AI Explanations

  • Built and ran zenvra scan on test-fixtures/vulnerable_app.py
  • Configured Groq API (llama-3.3-70b-versatile) for AI-powered explanations
  • Results: 7 hardcoded secrets detected (3 critical, 2 high, 2 medium)
  • AI explanations + fix suggestions generated via Groq in <1s per finding

3. NVD CVE Data Sync & Database Setup

  • Started PostgreSQL (5433) + Redis (6379) via docker compose
  • Ran migrations and synced NVD/OSV vulnerability data
  • Verified: Database contains CVE records for mapping

4. API Server Verification

  • Axum server running on localhost:8080
  • /api/v1/scan — accepts code, returns scan ID
  • /api/v1/scan/:id/events — streams results via SSE (real-time)
  • Tested: Secrets engine + AI enrichment working end-to-end

5. Web Frontend Setup

  • SvelteKit 5 dev server running on localhost:5174
  • Created .env.local for environment configuration
  • Scan page includes SAST + Secrets engines enabled by default
  • UI ready to accept code submissions

6. SAST Engine Verification

  • 10+ vulnerability detection rules already implemented:
    • Insecure hashing (MD5, SHA1)
    • SQL Injection detection
    • OS command injection
    • eval() and dangerous functions
    • XSS sinks (dangerouslySetInnerHTML, innerHTML)
    • Path traversal
    • Prototype pollution
    • Insecure randomness
    • Weak cryptography (DES, RC4)
    • Hardcoded localhost references
  • Tests: All 3 unit tests passing
  • Real-world test: Detected 5 vulnerabilities in test code (SQL injection, MD5, eval, command injection)
  • API integration: SAST findings streamed with AI explanations via SSE

7. Documentation

  • Created CONFIG_GUIDE.md documenting all configuration options
  • Environment variables mapped (PUBLIC_API_URL, etc.)
  • API endpoints listed with descriptions

🚀 Quick Start (All Services)

Terminal 1: PostgreSQL + Redis

cd zenvra
docker compose up -d postgres redis

Terminal 2: API Server

cd zenvra
set -a && source .env && set +a
cargo run -p zenvra-server

Terminal 3: Web Frontend

cd zenvra/apps/web
pnpm dev

Terminal 4: Optional — CLI Scans

cd zenvra
set -a && source .env && set +a
cargo run -p zenvra-cli -- scan <file_or_dir>

Then open http://localhost:5174 and:

  1. Go to Settings → AI to configure Groq API key
  2. Go to Scan page to submit code
  3. Watch real-time findings stream in

🎯 Features Verified

Component Status Details
Secrets Detection ✅ Working AWS keys, API tokens, private keys, passwords
SAST Engine ✅ Working SQL injection, XSS, command injection, weak crypto
AI Explanations ✅ Working Groq llama-3.3-70b (fast, free tier)
NVD CVE Sync ✅ Working PostgreSQL populated with vulnerability data
CLI ✅ Working zenvra scan <path> with multiple engines
API Server ✅ Working SSE streaming, real-time results
Web UI ✅ Ready SvelteKit 5, responsive, scan submission
Configuration ✅ Clean Env vars, localStorage for AI config

📝 Next Steps

  1. Test Web UI — Submit vulnerable code from browser, verify findings appear
  2. VS Code Extension — Integrate SAST + API with inline diagnostics
  3. SCA Engine — Implement dependency vulnerability scanning
  4. AI Code Patterns — Detect common AI-generated code anti-patterns
  5. Production Deployment — Docker images, environment scaling

🔗 Key Files

  • .env — Backend + AI config (Groq API, NVD key, DB URL)
  • apps/web/.env.local — Frontend config (API URL)
  • CONFIG_GUIDE.md — Full configuration reference
  • crates/scanner/src/engines/sast.rs — SAST rules (10+ patterns)
  • crates/server/src/main.rs — Axum API server
  • apps/web/src/routes/scan/+page.svelte — Web scan UI

Built with ❤️ using Rust + SvelteKit + Groq AI. Ship fast. Ship safe.