Gnoppix AI

Automating Code Security Review: A Practical Guide to Gnoppix Code Agent + LM Studio

For security professionals, code review is the backbone of building trustworthy software no matter if your target is a web service, API backend, desktop tool, or mobile app. Yet, the process often feels slow and prone to error when there are no professional scanning tools at hand. Hours are lost through human code review, searching for API endpoints, cryptographic keys, or other hidden secrets buried in thousands of files.

How about utilizing AI?

Most cloud-based AI tools have token limits that prevent them from processing entire projects. It doesn’t work. You need something local, private, and intelligent.

That’s where Gnoppix Code Agent combined with LM Studio comes in. This guide walks you through setting up an AI-powered local code review system that can analyze your entire mobile app project without uploading anything to the cloud.

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1. Getting Started: Installing Gnoppix Code Agent

Gnoppix Code Agent is a developer-friendly tool that runs right in your terminal. Setting it up is straightforward. If you are on Gnoppix 25/26, you will need to install it manually; however, on Gnoppix testing images, it comes pre-installed.

1.1 Installation

The easiest way to install Gnoppix Code Agent is using the universal installer:

sudo apt install opencode gnoppix-opencode-cli

1.2 Verifying Your Installation

Once installed, verify everything is working correctly:

opencode --version

Alternatively, you can launch the Code Agent from the menu, which includes a graphical user interface (GUI).

Configuration files will be stored in the ~/.oencode/ directory. You can launch Gnoppix Code Agent anytime from your terminal with:

opencode

1.3 Default Setup

By default, Gnoppix Code Agent uses Grok Code as its model, but it’s only available for limited use. That’s why we’ll connect it to LM Studio to use a fully local language model instead giving you unlimited access and complete privacy.

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2. Connecting LM Studio to Gnoppix Code Agent

This is where the magic happens. We’re going to connect your local language model running in LM Studio to Gnoppix Code Agent, creating a powerful AI-assisted code review setup.

2.1 Starting Your LM Studio Server

First, launch LM Studio and start your local language model. Make sure the server is running properly by confirming it’s accessible at http://localhost:1234 (the default port).

You can check which port LM Studio is using through Server Settings if you need to adjust it.

In this How2, the Qwen3 Coder 30B model was used, which relies heavily on understanding programming language context during code review tasks. Therefore, it is recommended choosing a model that best fits your hardware environment.

2.2 Configuring Gnoppix Code Agent

Now we need to tell Gnoppix Code Agent where to find your LM Studio server. Let’s create and open the configuration file with:

vim ~/.opencode/config.json

Paste the configuration:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "lmstudio": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "LM Studio (local)",
      "options": {
        "baseURL": "http://localhost:1234/v1"
      },
      "models": {
        "qwen/qwen3-coder-30b": {
          "name": "qwen3-coder-30b"
        }
      }
    }
  }
}

This tells Gnoppix Code Agent to recognize LM Studio as a local model provider, pointing to its API endpoint and defining available models. The “provider” section and API endpoint are required to enable local LLMs, while the $schema and models fields are recommended for validation and easier selection. Note that the model field is only needed if you want to set a default model automatically.

2.3 Testing the Connection

To make sure everything is connected properly, launch Gnoppix Code Agent and run:

/model

You should see “LM Studio (local)” appear as an available provider along with your models. If it shows up, you’re all set!

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3. Creating Custom Security Review Commands

Gnoppix Code Agent shines when you create custom commands tailored to your workflow. Let’s build an initial mobile app security review command named mobile-app-review-1. The goal is to let AI help with hunting all possible attack vectors.

3.1 Setting Up Your Commands Directory

Create a directory for your commands:

touch .opencode/commands/mobile-app-review-1.md

As you create more specialized commands, organize them like this:

.opencode/commands/
├── mobile-app-review-1.md
├── api-endpoint-scan.md
├── internet-asset-detection.md
└── dependency-audit.md

3.2 Writing Your Security Review Command

Now edit that file and add your review template. Here is an example:

# mobile-app-review-1

Perform a code review and write a markdown report for the authentication flow implemented in this mobile app.

The report should include:
- The main authentication API endpoint and full request URL format
- The exact HTTP request body structure for login, showing field names and data type
- Details about any secrets or key material stored or hardcoded, explaining their role
- A step-by-step summary of how authentication operates, including token usage for session management
- Any security risks observed in the approach

Format the output as a concise markdown technical report for security review.

Save the findings to a Report.md file.

This command performs a code review of a mobile app’s authentication flow and generates a concise markdown security report summarizing endpoints, request structures, secrets, authentication logic, and risks, saving results directly to a markdown file.

Nowadays, attackers frequently analyze clues within APK files to discover ways to target backend services. Therefore, when performing a security code review, it’s essential to identify these clues and help developers remediate potential vulnerabilities.

3.3 Running Your Custom Command

When you’re ready to review an app, open Gnoppix Code Agent and press / to open the command palette. Select the one we created /mobile-app-review-1, provide the absolute path of your review target, and hit Enter. The AI will start analyzing your project.

Remember, before any recon operation, explicit permission acquired from the service owner is strictly required!

3.3.1 Blackbox testing

Black-box testing (also known as specification-based testing) is a software testing method that evaluates an application’s functionality without inspecting its internal code, structures, or architecture.

This versatile approach can be applied to virtually every level and type of software testing:

• Unit testing
• Integration testing
• System testing
• Acceptance testing
• Vulnerability testing

3.3.2 Using gnoppixctl-gui

For black-box testing, we have developed an anonymizer application that routes all requests through the Tor network—absolutely all of them. Except for the exit node you are using, no one will know your identity, making this tool perfect for black-box testing your site. Please note that this can be illegal in a few countries. However, we believe that as a site owner, it is perfectly fine to test your application’s website infrastructure just as a remote attacker would. Vulnerability scanning is essential today.

3.4 Review Your Report

When finished, Gnoppix Code Agent will create a Report.md file containing all the findings. Verify them and start planning your further remediation or operations. Based on testing experience, the resulting output is highly accurate and comprehensive.

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Conclusion

No matter your project scope or the LLMs you choose, the power of your hardware unlocks access to even more advanced models delivering stronger analysis and deeper insights with every review. Furthermore, the approach covered here operates entirely locally, ensuring sensitive code and vulnerability findings never leave your secure environment. This is a solution truly designed for the practical realities of modern cybersecurity operations.