Usage Examples - HiddenLayer Docs

Examples use the Supply Chain CLI image from Deploy Supply Chain CLI.

Scanning a single file

Use the AI Supply Chain Security CLI to scan a single file stored locally on your system.

Local setup

HL_LICENSE is your HiddenLayer product license as an environment variable. See Running Supply Chain CLI for information about setting the HiddenLayer environment variables.
The model to be scanned is on the host machine at path /home/user/models/. Change this path to match your system.
The model to be scanned is named my_model.keras. Change this name to match the file you want to scan.
For Disconnected Mode:
- The scan results are saved at path /home/user/results. Change this path to match your system.
For Hybrid Mode:
- HL_CLIENT_ID and HL_CLIENT_SECRET are the API key and secret to allow communication with the HiddenLayer AI Security Platform.
- --persist adds the scan results to an existing model in the HiddenLayer Console.
- --model-name and --model-version are the name and version number that appear in the Supply Chain.

Container execution

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -v /home/user/models:/files-to-scan \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input /files-to-scan/my_model.keras --persist --model-name=<model_name_in_console> --model-version=<model_version>

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e HL_MODEL_SCANNER_PLATFORM_RESULTS_URL='https://api.eu.hiddenlayer.ai' \
  -e HL_MODEL_SCANNER_TOKEN_URL=https://api.eu.hiddenlayer.ai/ \
  -v /home/user/models:/files-to-scan \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input /files-to-scan/my_model.keras --persist --model-name=<model_name_in_console> --model-version=<model_version>

docker run --rm \
  -e HL_LICENSE \
  -v /home/user/models:/files-to-scan \
  -v /home/user/results:/scan-results \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input /files-to-scan/my_model.keras

Output Example - STDOUT

Scan results will be JSON-minified, but are shown formatted here for readability.

EXPAND to see an example for scan results.

    {
      "scan_id": "13c0f8a4-c938-43ee-9d95-2803ccd7de10",
      "start_time": "2025-02-12T17:55:36.511Z",
      "end_time": "2025-02-12T17:55:36.517Z",
      "status": "done",
      "version": "25.3.0",
      "$schema_version": "3.1.0",
      "inventory": {
        "requested_scan_location": "/files-to-scan/ZS_withConfig.nemo",
        "model_id": "00000000-0000-0000-0000-000000000000",
        "model_name": "cli-25.3.0-cdffd55e-802b-4440-8403-698e930e5bb9",
        "model_version": "1739382936",
        "model_version_id": "00000000-0000-0000-0000-000000000000"
      },
      "file_results": [
        {
          "file_instance_id": "01c8c57d-dd77-488b-b459-709f1933f7b2",
          "file_location": "/files-to-scan/ZS_withConfig.nemo",
          "status": "done",
          "start_time": "2025-02-12T17:55:36.511Z",
          "end_time": "2025-02-12T17:55:36.517Z",
          "details": {
            "sha256": "6a15d94d7c9a67d2574ba8226cfa7d678524899d61f18f98cb218da6e30f0570",
            "file_type": "TAR",
            "file_type_details": {},
            "estimated_time": ""
          },
          "seen": "2025-02-12T17:55:36.511Z",
          "detections": [
            {
              "detection_id": "b3598a21-bdb0-4f4d-9c66-c7f127894b43",
              "rule_id": "NEMO_0008_202408",
              "category": "Decompression Vulnerabilities",
              "description": "Archive Contains Tar Slip attack, which can be used to overwrite files on the system.",
              "severity": "high",
              "mitre_atlas": [
                {
                  "technique": "AML.T0010",
                  "tactic": "AML.TA0004"
                }
              ],
              "owasp": [
                "ML06",
                "LLM05"
              ],
              "cwe": "",
              "cwe_href": ""
            }
          ]
        }
      ],
      "detection_count": 1,
      "file_count": 1,
      "files_with_detections_count": 1,
      "summary": {
        "detection_count": 1,
        "file_count": 1,
        "severity": "high",
        "files_with_detections_count": 1,
        "detection_categories": [
          "Decompression Vulnerabilities"
        ]
      }
    }

Scanning a directory

Use the Supply Chain CLI to scan all files in a directory that is stored locally on your system.

Local Setup

HL_LICENSE is your HiddenLayer product license as an environment variable. See Running Supply Chain CLI for information about setting the HiddenLayer environment variables.
The models to be scanned are on the host machine at path /home/user/models/. Change this path to match your system.
For Disconnected Mode:
- The scan results are saved at path /home/user/results. Change this path to match your system.
For Hybrid Mode:
- HL_CLIENT_ID and HL_CLIENT_SECRET are the API key and secret to allow communication with the HiddenLayer AI Security Platform.

Container execution

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -v /home/user/models:/files-to-scan \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input /files-to-scan/

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e HL_MODEL_SCANNER_PLATFORM_RESULTS_URL='https://api.eu.hiddenlayer.ai' \
  -e HL_MODEL_SCANNER_TOKEN_URL=https://api.eu.hiddenlayer.ai/ \
  -v /home/user/models:/files-to-scan \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input /files-to-scan/

docker run --rm \
  -e HL_LICENSE \
  -v /home/user/models:/files-to-scan \
  -v /home/user/results:/scan-results \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input /files-to-scan/

Output Example - STDOUT

Scan results will be JSON-minified, but are shown formatted here for readability.

EXPAND to see an example for scan results.

    {
            "scan_id": "935fe7b5-ebc2-4a34-9c85-f1dd9f71b464",
            "start_time": "2024-12-13T05:58:26.306Z",
            "end_time": "2024-12-13T05:58:46.163Z",
            "status": "done",
            "version": "24.10.3",
            "$schema_version": "3.1.0",
            "inventory": {
                    "requested_scan_location": "/files-to-scan/",
                    "model_id": "00000000-0000-0000-0000-000000000000",
                    "model_version_id": "d6ee11d6-31e6-4e28-8382-cda6a49c0c3e"
            },
            "file_results": [
                    {
                            "file_instance_id": "2c975b09-b9bf-4bf4-8d80-9ee45d344b8b",
                            "file_location": "/files-to-scan/aws_model.safetensors",
                            "status": "done",
                            "start_time": "2024-12-13T05:58:26.306Z",
                            "end_time": "2024-12-13T05:58:37.203Z",
                            "details": {
                                    "sha256": "eed458758bd8165d80f496a90bcd2cfed9f1bf7d7b08677acd64e0f7d72bdcf2",
                                    "file_type": "safetensors",
                                    "file_type_details": {},
                                    "estimated_time": ""
                            },
                            "seen": "2024-12-13T05:58:26.306Z",
                            "detections": []
                    },
                    {
                            "file_instance_id": "ba1a10f9-5113-4520-b03a-0e4c0970fa25",
                            "file_location": "/files-to-scan/test_model.pkl",
                            "status": "done",
                            "start_time": "2024-12-13T05:58:46.003Z",
                            "end_time": "2024-12-13T05:58:46.163Z",
                            "details": {
                                    "sha256": "bce834158f08706277377c50226e31b74e372770fe26fc5bdb4ff74de9924ffa",
                                    "file_type": "pickle",
                                    "file_type_details": {
                                            "pickle_header": {
                                                    "pickle_version": "3",
                                                    "type": "pickle"
                                            },
                                            "pickle_modules": [
                                                    "sklearn.pipeline.pipeline",
                                                    "sklearn.preprocessing._data.standardscaler",
                                                    "callable: numpy.core.multiarray.scalar",
                                                    "callable: numpy.dtype",
                                                    "callable: numpy.core.multiarray._reconstruct",
                                                    "numpy.ndarray",
                                                    "lightgbm.sklearn.lgbmclassifier",
                                                    "lightgbm.basic.booster",
                                                    "callable: collections.defaultdict",
                                                    "collections.ordereddict",
                                                    "sklearn.preprocessing._label.labelencoder"
                                            ],
                                            "subtype": [
                                                    "numpy",
                                                    "scikit"
                                            ]
                                    },
                                    "estimated_time": ""
                            },
                            "seen": "2024-12-13T05:58:46.003Z",
                            "detections": []
                    }
            ],
            "detection_count": 1,
            "file_count": 3,
            "files_with_detections_count": 1,
            "summary": {
        "detection_count": 1,
        "file_count": 3,
        "severity": "high",
        "files_with_detections_count": 1,
        "detection_categories": [
          "Decompression Vulnerabilities"
        ]
      }
    }

Globbing File Paths

PreviewThis feature is in Preview.

Scan all .pkl files in the current directory and all subdirectories: --input . --include-pattern "*.pkl"
Scan all files in the directory tree rooted at the directory models, excluding .txt files: --input ./models --exclude-pattern "*.txt"
Scan all .onnx files in the directory tree rooted at the current working directory, except those labeled with a “v1” in the name: modelscan-cli . --include-pattern "*.onnx" --exclude-pattern "*v1*"
Scan all files except .json and .txt files in the current directory and all subdirectories: --input . --exclude-pattern "*.json" --exclude-pattern "*.txt"
Scan all files in the current directory and all subdirectories that start with pytorch_model-: --input . --include-pattern "pytorch_model-*.bin"

Azure Blob Storage

URL for Single Blob with Shared Access Signature

An HTTPS URL with a shared access signature can be used with the —input parameter, but only a single blob is supported.

Blob Enumeration

Multiple blobs in a single container can be scanned by using a wasbs:// URI with the —input parameter. The URI should be of the following form: wasbs://your-container-name@your-storage-account.blob.core.windows.net/your-optional-prefix Depending on the storage configuration, credentials may be required. Set the following environment variables:

AZURE_TENANT_ID
AZURE_CLIENT_ID
AZURE_CLIENT_SECRET

Download Model from Azure

If none of the previous methods are suitable, the model can be downloaded from Azure, then scanned using the Supply Chain CLI’s support for local files. This example uses the Azure CLI to download the file. To install the Azure CLI, see the Azure documentation. After installing the Azure CLI, configure authentication for the Azure CLI.

Download a Model from Azure to Local

Replace <storage-account> with the name of the Azure storage account.
Replace <container> with the name of the Azure Blob container. Replace <model-name> with the file name you are downloading.
Replace <~/destination/path/for/file> with the path and file name for your local storage (example: ~/Downloads/pytorch_model.bin).

az storage blob download \
  --account-name <storage-account> \
  --container-name <container> \
  --name <file-name> \
  --file <~/destination/path/for/file> \
  --auth-mode login

After downloading the file, use Scanning a single file to scan the local model.

Scan Model from Amazon S3

Presigned URL for Single Object

An HTTPS URL that is presigned can be used with the --input parameter, but only a single object is supported.

HL_LICENSE is your HiddenLayer product license as an environment variable. See Running Supply Chain CLI for information about setting the HiddenLayer environment variables.
Replace presigned_url with your AWS S3 presigned URL for the model file you want to scan.
For Disconnected Mode:
- The scan results are saved at path /home/user/results. Change this path to match your system.
For Hybrid Mode:
- HL_CLIENT_ID and HL_CLIENT_SECRET are the API key and secret to allow communication with the HiddenLayer AI Security Platform.
- --persist adds the scan results to an existing model in the Supply Chain.
- --model-name and --model-version are the name and version number that appear in the Supply Chain.

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e AWS_ACCESS_KEY_ID=<access_key> \
  -e AWS_SECRET_ACCESS_KEY=<secret_key> \
  -e AWS_SESSION_TOKEN=<session_token> \
  -e AWS_REGION=<aws_region> \
  -e AWS_ENDPOINT=<endpoint_url> \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input "presigned_url" --persist --model-name=<model_name_in_console> --model-version=<model_version>

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e HL_MODEL_SCANNER_PLATFORM_RESULTS_URL='https://api.eu.hiddenlayer.ai' \
  -e HL_MODEL_SCANNER_TOKEN_URL=https://api.eu.hiddenlayer.ai/ \
  -e AWS_ACCESS_KEY_ID=<access_key> \
  -e AWS_SECRET_ACCESS_KEY=<secret_key> \
  -e AWS_SESSION_TOKEN=<session_token> \
  -e AWS_REGION=<aws_region> \
  -e AWS_ENDPOINT=<endpoint_url> \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input "presigned_url" --persist --model-name=<model_name_in_console> --model-version=<model_version>

docker run --rm \
  -e HL_LICENSE \
  -e AWS_ACCESS_KEY_ID=<access_key> \
  -e AWS_SECRET_ACCESS_KEY=<secret_key> \
  -e AWS_SESSION_TOKEN=<session_token> \
  -e AWS_REGION=<aws_region> \
  -e AWS_ENDPOINT=<endpoint_url> \
  -v /home/user/results:/scan-results \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input "presigned_url"

Bucket Enumeration

Multiple objects in a single bucket can be scanned by using a s3:// URI with the --input parameter. The URI should be of the following form: s3://some-bucket/some-optional-prefix Depending on the bucket policy, AWS credentials may be required.

HL_LICENSE is your HiddenLayer product license as an environment variable. See Running Supply Chain CLI for information about setting the HiddenLayer environment variables.
Replace AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_SESSION_TOKEN, AWS_REGION, and AWS_ENDPOINT with your AWS information.
Replace s3://some-bucket/some-optional-prefix with your AWS S3 bucket information for the bucket you want to scan.
For Disconnected Mode:
- The scan results are saved at path /home/user/results. Change this path to match your system.
For Hybrid Mode:
- HL_CLIENT_ID and HL_CLIENT_SECRET are the API key and secret to allow communication with the HiddenLayer AI Security Platform.

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e AWS_ACCESS_KEY_ID=<access_key> \
  -e AWS_SECRET_ACCESS_KEY=<secret_key> \
  -e AWS_SESSION_TOKEN=<session_token> \
  -e AWS_REGION=<aws_region> \
  -e AWS_ENDPOINT=<endpoint_url> \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input s3://some-bucket/some-optional-prefix

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e HL_MODEL_SCANNER_PLATFORM_RESULTS_URL='https://api.eu.hiddenlayer.ai' \
  -e HL_MODEL_SCANNER_TOKEN_URL=https://api.eu.hiddenlayer.ai/ \
  -e AWS_ACCESS_KEY_ID=<access_key> \
  -e AWS_SECRET_ACCESS_KEY=<secret_key> \
  -e AWS_SESSION_TOKEN=<session_token> \
  -e AWS_REGION=<aws_region> \
  -e AWS_ENDPOINT=<endpoint_url> \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input s3://some-bucket/some-optional-prefix

docker run --rm \
  -e HL_LICENSE \
  -e AWS_ACCESS_KEY_ID=<access_key> \
  -e AWS_SECRET_ACCESS_KEY=<secret_key> \
  -e AWS_SESSION_TOKEN=<session_token> \
  -e AWS_REGION=<aws_region> \
  -e AWS_ENDPOINT=<endpoint_url> \
  -v /home/user/results:/scan-results \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input s3://some-bucket/some-optional-prefix

Scan Model from Google Cloud Storage

Multiple objects in a single bucket can be scanned by using a gs:// URI with the --input parameter.

HL_LICENSE is your HiddenLayer product license as an environment variable. See Running Supply Chain CLI for information about setting the HiddenLayer environment variables.
Create a credentials file (credentials.json) for the service account. See Google documentation for more information.
Replace gs://some-bucket/some-prefix with your Google storage information for the bucket you want to scan.
For Disconnected Mode:
- The scan results are saved at path /home/user/results. Change this path to match your system.
For Hybrid Mode:
- HL_CLIENT_ID and HL_CLIENT_SECRET are the API key and secret to allow communication with the HiddenLayer AI Security Platform.

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -v $PWD/credentials.json:/credentials.json \
  -e GOOGLE_APPLICATION_CREDENTIALS=/credentials.json \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input gs://some-bucket/some-prefix

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e HL_MODEL_SCANNER_PLATFORM_RESULTS_URL='https://api.eu.hiddenlayer.ai' \
  -e HL_MODEL_SCANNER_TOKEN_URL=https://api.eu.hiddenlayer.ai/ \
  -v $PWD/credentials.json:/credentials.json \
  -e GOOGLE_APPLICATION_CREDENTIALS=/credentials.json \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input gs://some-bucket/some-prefix

docker run --rm \
  -e HL_LICENSE \
  -v $PWD/credentials.json:/credentials.json \
  -e GOOGLE_APPLICATION_CREDENTIALS=/credentials.json \
  -v /home/user/results:/scan-results \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input gs://some-bucket/some-prefix

Scan a Hugging Face Repository

Scan the files within a Hugging Face repository by providing the Hugging Face repository URL in the scan request. Example: https://huggingface.co/username/repo_name. When scanning a Hugging Face repository, the Supply Chain CLI will:

Identify and download all files within the specified repository.
Perform the scanning process on each downloaded file, according to the standard scanning rules and logic of the Supply Chain CLI.
Generate a scan report.

If an invalid or non-existent Hugging Face repository URL is used, an error message is returned, and no scanning is performed. Use the following command to scan a Hugging Face repository.

Hugging Face Repository

Use the following command to scan a Hugging Face repository.

HL_LICENSE is your HiddenLayer product license as an environment variable. See Running Supply Chain CLI for information about setting the HiddenLayer environment variables.
Replace <username>/<repo_name> with the appropriate Hugging Face repository information.
For Disconnected Mode:
- The scan results are saved at path /home/user/results. Change this path to match your system.
For Hybrid Mode:
- HL_CLIENT_ID and HL_CLIENT_SECRET are the API key and secret to allow communication with the HiddenLayer AI Security Platform.
- --persist adds the scan results to an existing model in the Supply Chain.

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input https://huggingface.co/<username>/<repo_name> --persist

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e HL_MODEL_SCANNER_PLATFORM_RESULTS_URL='https://api.eu.hiddenlayer.ai' \
  -e HL_MODEL_SCANNER_TOKEN_URL=https://api.eu.hiddenlayer.ai/ \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input https://huggingface.co/<username>/<repo_name> --persist

docker run --rm \
  -e HL_LICENSE \
  -v /home/user/results:/scan-results \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input https://huggingface.co/<username>/<repo_name>

Private or Gated Hugging Face Repository

To scan a private or gated Hugging Face repository, include the Hugging Face token as an environment variable in the scan request.

HL_LICENSE is your HiddenLayer product license as an environment variable. See Running Supply Chain CLI for information about setting the HiddenLayer environment variables.
Replace <username>/<repo_name> with the appropriate Hugging Face repository information.
Replace <hf-token> with the Hugging Face token for the private or gated repository.
For Disconnected Mode:
- The scan results are saved at path /home/user/results. Change this path to match your system.
For Hybrid Mode:
- HL_CLIENT_ID and HL_CLIENT_SECRET are the API key and secret to allow communication with the HiddenLayer AI Security Platform.
- --persist adds the scan results to an existing model in the Supply Chain.

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e HF_TOKEN=<hf-token> \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input https://huggingface.co/<username>/<repo_name> --persist

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e HL_MODEL_SCANNER_PLATFORM_RESULTS_URL='https://api.eu.hiddenlayer.ai' \
  -e HL_MODEL_SCANNER_TOKEN_URL=https://api.eu.hiddenlayer.ai/ \
  -e HF_TOKEN=<hf-token> \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input https://huggingface.co/<username>/<repo_name> --persist

docker run --rm \
  -e HL_LICENSE \
  -e HF_TOKEN=<hf-token> \
  -v /home/user/results:/scan-results \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input https://huggingface.co/<username>/<repo_name>

Scan a NIM Container

Scan a NVIDIA NIM container image using an oci://nvcr.io/nim/... input. NIM container scanning is available in Supply Chain version 26.6.0 and later. See NIM Container Scanning for manifest parsing, referenced models, and follow-up scan workflows.

Local setup

HL_LICENSE is your HiddenLayer product license as an environment variable. See Running Supply Chain CLI for information about setting the HiddenLayer environment variables.
NGC_API_KEY is required to pull NIM images from NVIDIA’s container registry. Create an API key in the NGC portal.
Replace oci://nvcr.io/nim/nvidia/test-model:latest with the NIM image reference you want to scan.
For Disconnected Mode:
- The scan results are saved at path /home/user/results. Change this path to match your system.
For Hybrid Mode:
- HL_CLIENT_ID and HL_CLIENT_SECRET are the API key and secret to allow communication with the HiddenLayer AI Security Platform.
- --persist adds the scan results to an existing model in the HiddenLayer Console.
- --model-name and --model-version are the name and version number that appear in the Supply Chain.

Container execution

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e NGC_API_KEY \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input oci://nvcr.io/nim/nvidia/test-model:latest \
  --persist --model-name=<model_name_in_console> --model-version=<model_version>

docker run --rm \
  -e HL_LICENSE \
  -e HL_CLIENT_ID \
  -e HL_CLIENT_SECRET \
  -e HL_MODEL_SCANNER_PLATFORM_RESULTS_URL='https://api.eu.hiddenlayer.ai' \
  -e HL_MODEL_SCANNER_TOKEN_URL=https://api.eu.hiddenlayer.ai/ \
  -e NGC_API_KEY \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input oci://nvcr.io/nim/nvidia/test-model:latest \
  --persist --model-name=<model_name_in_console> --model-version=<model_version>

docker run --rm \
  -e HL_LICENSE \
  -e NGC_API_KEY \
  -v /home/user/results:/scan-results \
  images.hiddenlayer.ai/proxy/supply-chain-security/ghcr.io/hiddenlayer-engineering/supply-chain-scanner:26.9.0 \
  --input oci://nvcr.io/nim/nvidia/test-model:latest \
  --output /scan-results/nim-container-scan.json

​Scanning a single file

​Local setup

​Container execution

​Output Example - STDOUT

​Scanning a directory

​Local Setup

​Container execution

​Output Example - STDOUT

​Globbing File Paths

​Azure Blob Storage

​URL for Single Blob with Shared Access Signature

​Blob Enumeration

​Download Model from Azure

​Download a Model from Azure to Local

​Scan Model from Amazon S3

​Presigned URL for Single Object

​Bucket Enumeration

​Scan Model from Google Cloud Storage

​Scan a Hugging Face Repository

​Hugging Face Repository

​Private or Gated Hugging Face Repository

​Scan a NIM Container

​Local setup

​Container execution

Scanning a single file

Local setup

Container execution

Output Example - STDOUT

Scanning a directory

Local Setup

Container execution

Output Example - STDOUT

Globbing File Paths

Azure Blob Storage

URL for Single Blob with Shared Access Signature

Blob Enumeration

Download Model from Azure

Download a Model from Azure to Local

Scan Model from Amazon S3

Presigned URL for Single Object

Bucket Enumeration

Scan Model from Google Cloud Storage

Scan a Hugging Face Repository

Hugging Face Repository

Private or Gated Hugging Face Repository

Scan a NIM Container

Local setup

Container execution