NVIDIA SkillSpector Guide: Scanning AI Skills for Security Risks with Static Analysis and SARIF Reports

Introduction

In this tutorial, we'll learn how to use NVIDIA SkillSpector to scan AI skills for security risks. SkillSpector is a tool that helps developers identify potential security vulnerabilities in AI applications before they're deployed. This is especially important as AI systems become more integrated into critical applications. We'll build a simple AI skill, scan it for risks, and analyze the results using Python and pandas. By the end, you'll understand how to perform static analysis on AI skills and interpret the findings in a structured way.

Prerequisites

Before starting this tutorial, you should have:

• Basic understanding of Python programming
• Python 3.8 or higher installed
• Access to NVIDIA SkillSpector (either via their API or local installation)
• Installed Python packages: pandas, requests, and json

Why we need these: Python is the main language for data analysis and scripting in this tutorial. SkillSpector requires an API or local installation to perform scans. We'll use pandas to organize and analyze the results, and requests to communicate with the tool.

Step-by-Step Instructions

1. Install Required Python Packages

First, make sure you have the required Python packages installed. Run this command in your terminal:

pip install pandas requests

Why we install these: pandas will help us organize the scan results, and requests allows us to send data to SkillSpector's API.

2. Prepare Your AI Skill Code

Let's create a simple AI skill that we'll scan. This skill will ask for user input and respond. Save this code in a file named simple_skill.py:

def main():
    user_input = input("Enter your name: ")
    print(f"Hello, {user_input}!")

if __name__ == "__main__":
    main()

Why we create this: This is a basic skill that simulates user interaction. We'll scan it to see if SkillSpector can detect any potential security issues.

3. Prepare the Scan Request

Next, we need to prepare a request to send to SkillSpector. Create a Python script named scan_skill.py with the following code:

import requests
import json

# Define the API endpoint for SkillSpector
api_url = "https://api.skillspector.com/scan"

# Prepare the payload
payload = {
    "skill_code": open("simple_skill.py", "r").read(),
    "skill_name": "Simple Skill",
    "scan_type": "static"
}

# Send the request
response = requests.post(api_url, json=payload)

# Print the response
print(json.dumps(response.json(), indent=2))

Why we do this: This script sends the skill code to SkillSpector for analysis. The scan_type is set to static, which means we're analyzing the code without running it.

4. Analyze the Scan Results

After running the scan, SkillSpector returns a JSON response with security findings. We'll process this data using pandas to organize it:

import pandas as pd
import json

# Load the response from the scan
with open("scan_results.json", "r") as f:
    scan_data = json.load(f)

# Extract findings
findings = scan_data.get("findings", [])

# Create a DataFrame
df = pd.DataFrame(findings)

# Display the DataFrame
print(df)

# Show summary statistics
print(df.groupby("severity").size())

Why we do this: Using pandas makes it easy to analyze and visualize the scan results. We can group findings by severity to quickly see which issues are most critical.

5. Export Results in SARIF Format

SARIF (Static Analysis Results Interchange Format) is a standard format for security scan results. Let's export our findings:

import json

# Create a SARIF output
sarif_output = {
    "$schema": "https://raw.githubusercontent.com/oasis-tcs/sarif-spec/master/Schemata/sarif-schema-2.1.0.json",
    "version": "2.1.0",
    "runs": [
        {
            "tool": {
                "driver": {
                    "name": "SkillSpector",
                    "version": "1.0"
                }
            },
            "results": []
        }
    ]
}

# Add findings to SARIF
for finding in findings:
    sarif_output["runs"][0]["results"].append({
        "ruleId": finding["rule_id"],
        "message": {
            "text": finding["description"]
        },
        "locations": [
            {
                "physicalLocation": {
                    "artifactLocation": {
                        "uri": "simple_skill.py"
                    }
                }
            }
        ]
    })

# Save to file
with open("scan_results.sarif", "w") as f:
    json.dump(sarif_output, f, indent=2)

print("SARIF file created successfully!")

Why we do this: SARIF format allows other tools and platforms to understand and use the scan results, making it easier to integrate SkillSpector into existing workflows.

6. Visualize the Results

Finally, let's create a simple visualization of our findings:

import matplotlib.pyplot as plt

# Count findings by severity
severity_counts = df["severity"].value_counts()

# Create a bar chart
plt.figure(figsize=(8, 5))
severity_counts.plot(kind='bar', color=['red', 'orange', 'yellow', 'green'])
plt.title('Security Findings by Severity')
plt.xlabel('Severity Level')
plt.ylabel('Number of Findings')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Why we do this: Visualizing the data helps quickly identify the most critical issues and provides a clear overview of the security risks in your AI skill.

Summary

In this tutorial, we've learned how to use NVIDIA SkillSpector to scan AI skills for security risks. We created a simple skill, sent it for analysis, processed the results using pandas, exported them in SARIF format, and visualized the findings. This workflow helps developers proactively identify potential security vulnerabilities in their AI applications before deployment. As AI becomes more prevalent, tools like SkillSpector will be essential for ensuring safe and secure AI systems.