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ML Model Static Scans

AccuKnox provides static analysis for Machine Learning (ML) models to identify vulnerabilities, security risks, and compliance issues. This guide walks you through setting up and running ML Model Static Scans for models hosted on GitHub and Hugging Face.

Why are ML Scans Needed?

Machine Learning models are becoming integral parts of modern applications, but they also introduce new attack vectors. ML Static Scans are essential for:

  • Vulnerability Detection: Identifying known vulnerabilities in model files (e.g., pickle deserialization attacks).
  • Supply Chain Security: Ensuring that models imported from public repositories (like Hugging Face) are safe to use.
  • Compliance: Meeting security standards by scanning AI/ML assets for risks.
  • Model Integrity: Verifying that model weights and configurations have not been tampered with.

Setting up an ML Static Scan

Follow these steps to configure a scan for your ML models.

Step 1: Add a New Collector

  1. Navigate to Settings > Collectors in the AccuKnox dashboard.
  2. Click on Add Collector.
  3. Select ML-Static-Scan from the available options.
  4. Choose your platform: GitHub or Hugging Face, depending on where your models reside.

Select ML Static Scan

Step 2: Configure Repository Details

Enter the required credentials and repository information.

For GitHub:

  • Token: Your GitHub Personal Access Token (PAT).
  • Repository Owner: The username or organization name.
  • Repository Name: The name of the repository.
  • Repository Branch: The branch to scan (e.g., main).

For Hugging Face:

  • Token: Your Hugging Face Access Token.
  • Repository Name: The name of the model repository (e.g., bert-base-uncased).

Configure Repository

Step 3: Add Identifiers

Add Labels and Tags to categorize your ML models. This helps in filtering and managing assets within the AccuKnox platform.

Step 4: Validate Connection

Once you have entered the credentials, click on Test to validate the connection to your repository. Ensure the validation is successful before proceeding.

Step 5: Schedule the Scan

Configure the Schedule and Notification settings:

  1. Under Cron Job, define the frequency of the scan (e.g., daily, weekly).
  2. The system will display the Next Scan time based on your configuration.

Schedule Scan

Click Save to create the collector.

Viewing Scan Results

Supported Model Formats

AccuKnox currently supports scanning for the following ML model formats:

  • Pickle: Python object serialization.
  • HDF5/H5: Hierarchical Data Format used in Keras.
  • TensorFlow SavedModel: TensorFlow's native serialization format.
  • Model Checkpoints: Periodic snapshots of training progress.
  • ONNX: An interoperable format for sharing models across different tools.

Once the scan is triggered (either manually or via the schedule), AccuKnox will analyze the repository.

  1. Go to Issues or the Collectors page.
  2. Click on the Findings count for your ML Static Scan collector.

ML Findings

You will be redirected to the ML Findings page, which lists details such as:

  • Vulnerability Name: The specific issue detected.
  • Model Path: Location of the vulnerable file.
  • Source: The repository source.
  • Scanner & Operator: Tools used for detection.
  • Last Seen: Timestamp of the latest detection.

ML Findings List

Analyzing Findings

Click on any finding to open the Detailed Pane. Here you can:

  • View a detailed Description of the vulnerability.

  • Create Ticket: Integrate with your ticketing system (e.g., Jira) to track remediation.

  • Ask AI: Use the "Ask AI" button for assisted remediation advice.

Detailed Finding View

  • See the recommended Solution. Remediation Advice

  • Access Raw Information for in-depth analysis. Additional Details