OpenCode Setup for Cybersecurity Labs

Written by Aryan Giri

🚀 Intro

OpenCode is a customizable AI CLI that can act as your cybersecurity co-pilot for CTFs, bug bounty hunting, and hands-on labs. Instead of being locked into one AI provider, it lets you plug in multiple providers (OpenAI, OpenRouter, Nvidia, or even local models like Ollama).

With the right setup, it becomes a powerful assistant for recon, enumeration, exploitation, and debugging payloads.

⚙️ Installation Guide

Step 1 — Update Your System

Always start clean:

sudo apt update && sudo apt upgrade

Step 2 — Install OpenCode

curl -fsSL https://opencode.ai/install | bash

This will automatically download and install OpenCode.

Step 3 — Reload Your Shell

Installer usually updates your shell config (.zshrc or .bashrc):

source ~/.zshrc

Step 4 — Launch OpenCode

opencode

You’ll see a clean UI.

Type / to open commands.

You can either:

• Select /connect
• Or type /connect and press Enter

🔌 Connecting an AI Provider

You’ll see multiple providers:

• OpenRouter
• OpenAI
• Localhost (LM Studio / Ollama)
• Others

For beginners (especially without a GPU), Nvidia is a solid choice because:

• Higher rate limits
• Access to multiple LLMs
• Good for experimentation

Scroll and select Nvidia:

Step 5 — Get Nvidia API Key

Go to:

• https://build.nvidia.com/

Create an account (phone verification required).

Then visit:

• https://build.nvidia.com/settings/api-keys

Copy your API key and paste it into OpenCode.

Step 6 — Select a Model

After entering API key:

• Either type /models
• Or it will prompt automatically

Recommended model shown here:

Step 7 — Confirm UI

You should now see something like:

🧠 Creating a Cybersecurity Agent

By default, OpenCode is tuned for software engineering — not offensive security.

So we fix that.

Step 8 — Exit OpenCode

Press:

Ctrl + C

(Once or twice depending on your shell)

Step 9 — Create an Agent

opencode agent create

Paste this description:

A focused AI co-pilot designed for CTFs, bug bounty hunting, and hands-on cybersecurity labs. It guides users through real-world attack paths—from reconnaissance and enumeration to exploitation and post-exploitation—while explaining the underlying vulnerabilities, tools, and techniques. Built for practical learning, it helps debug failed exploits, craft payloads, and think like an attacker in modern security environments.

Example:

Step 10 — Capabilities

Just press Enter to accept defaults.

Step 11 — Select Your Agent

Launch OpenCode again:

opencode

Then type:

/agents

Select your newly created agent (based on description):

Step 12 — Ready State

Now your prompt should show:

• Agent Name
• Model Name
• Provider

⚠️ Important Notes

• AI can give false positives — always verify manually
• Do NOT rely blindly during real engagements

For Professionals:

If you're a:

• Pentester
• Red Teamer
• Blue Teamer

Avoid external providers (Nvidia, OpenAI, etc.) unless:

• You have permission
• It complies with company policies

Otherwise:

• Use LM Studio
• Use Ollama
• Use llama.cpp (fast + recommended)

🧪 Reality Check

• OpenCode is highly customizable
• Works with local + cloud models
• Great for learning workflows

But:

• OpenAI Codex and Gemini CLI often perform better out-of-the-box
• API versions may have lower rate limits compared to OAuth tools

🧠 Final Thoughts

OpenCode becomes powerful only when:

• You pick the right model
• You tune the agent correctly
• You validate outputs like a real attacker

Treat it as an assistant — not a replacement.

🔥 Now you’ve got the setup.

Next move?

Spin up a lab, throw a vulnerable machine at your agent, and see how it handles recon → exploit.

Where does it fail? That’s where you level up.