GSP1271

Overview

Gemini 2.0 Flash is a generative AI model purpose-built for diverse multimodal applications. It's proficiency in understanding and generating content across text, code, and images makes it a powerful asset for intricate codebase analysis. With its expansive 2M token context window, Gemini 2.0 Flash efficiently processes large code volumes in a single call, streamlining large-scale code scanning.Gemini 2.0 Flash's deep comprehension of programming languages and security best practices enables it to identify potential vulnerabilities and suggest helpful and contextual modifications. Learn more about Gemini 2.0 Flash.

This experimental approach aims to efficiently scan large codebases, analyze multiple files in a single call, and delve deeper into complex code relationships and patternsThe model's deep analysis of code can help ensure comprehensive vulnerability detection, going beyond surface-level flaws. By using this approach, we can accommodate code written in several programming languages. Additionally, we can generate the findings and recommendations as JSON or CSV reports, which we would hypothetically use to make comparisons against established benchmarks and policy checks.

Prerequisites

Before starting this lab, you should be familiar with:

• Basic Python programming.
• General API concepts.
• Running Python code in a Jupyter notebook on Vertex AI Workbench.

Objectives

In this lab, you learn how to use the Gemini API in Vertex AI, Google Cloud Storage API and the Google Gen AI SDK to work with the Gemini 2.0 Flash model to build a step by step code vulnerability scanning approach using Gemini 2.0 Flash:

• Read Python files from a Cloud Storage bucket and combining them into a single string
• Prompt engineering by crafting a clear and comprehensive prompt for Gemini 2.0 Flash, providing instructions for code analysis and output formatting
• Submit the consolidated code string to Gemini 2.0 Flash for analysis
• Extract vulnerability information, recommendations, and code snippets from the model response
• Generate CSV and JSON output reports for further analysis, benchmarking and integration with security tools

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Task 1. Open the notebook in Vertex AI Workbench

1. In the Google Cloud console, on the Navigation menu (), click Vertex AI > Workbench.

2. Find the instance and click on the Open JupyterLab button.

The JupyterLab interface for your Workbench instance opens in a new browser tab.

Note: If you do not see notebooks in JupyterLab, please follow these additional steps to reset the instance:

1. Close the browser tab for JupyterLab, and return to the Workbench home page.

2. Select the checkbox next to the instance name, and click Reset.

3. After the Open JupyterLab button is enabled again, wait one minute, and then click Open JupyterLab.

Task 2. Set up the notebook

1. Open the file.

2. In the Select Kernel dialog, choose Python 3 from the list of available kernels.

3. Run through the Getting Started and the Import libraries sections of the notebook.

   • For Project ID, use , and for Location, use .

Note: You can skip any notebook cells that are noted Colab only. If you experience a 429 response from any of the notebook cell executions, wait 1 minute before running the cell again to proceed.

Task 3. Process Python files in batch

In this section, you read Python files from a Cloud Storage bucket, combines their content and add a respective filename as separator for LLM to better identify each file.

1. Run through the Process Python files in batch section of the notebook.

Click Check my progress to verify the objective. Process Python files in batch

Task 4. Capture the model response into a single variable

In this section, you retrieve the response of code vulnerability analysis and store the results.

1. Run through the Capture the model response into a single variable section of the notebook.

Click Check my progress to verify the objective. Capture the model response into a single variable

Task 5. Parse the response and export it into JSON output

In this section, you parse the response into JSON and view the results.

1. Run through the Parse the response and export it into JSON output section of the notebook.

Click Check my progress to verify the objective. Parse the response and export it into JSON output

Congratulations!

You learned how to use the Gemini API in Vertex AI, Google Cloud Storage API and the Google Gen AI SDK to work with the Gemini 2.0 Flash model to build a step by step code vulnerability scanning approach using Gemini 2.0 Flash.

Next steps / learn more

Check out the following resources to learn more about Gemini:

• Gemini Overview
• Generative AI on Vertex AI Documentation
• Generative AI on YouTube
• Explore the Vertex AI Cookbook for a curated, searchable gallery of notebooks for Generative AI.
• Explore other notebooks and samples in the Google Cloud Generative AI repository.

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Manual Last Updated July 11, 2025

Lab Last Tested July 11, 2025

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