amazon-sagemaker-jupyter-scheduler

v3.2.2 suspicious
5.0
Medium Risk

Amazon SageMaker Jupyter Scheduler based on the https://pypi.org/project/jupyter-scheduler/

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential insecure handling of AWS credentials and the author having a single package, which raises suspicion about its legitimacy.

  • credential risk due to retrieval of AWS environment variables
  • author has only one package listed
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: The shell execution patterns seem to be related to Jupyter Lab extension management and configuration, which could be part of the package's functionality but should be reviewed further for context.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: The code appears to be retrieving AWS environment variables for region and account ID which could indicate legitimate use but also poses a risk if not handled securely.
  • Metadata: The author 'Amazon' has only one package, which could indicate a new or less active account, raising some suspicion.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • 9 test file(s) detected (e.g. utils.py)
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (344 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 119 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: result = subprocess.run( ["jupyter", "labextension", "list"],
  • ironman_service_config_pkg = subprocess.check_output( ["brazil-path", f"[{IRONMAN_CONFIG_PACKAGE_NAME}]ru
⚠ Credential Harvesting score 10.0

Found 5 credential access pattern(s)

  • gion_config_chain = [ os.environ.get( "AWS_REGION" ), # this value is set for Studio, so we do
  • nfig().get("region"), os.environ.get("AWS_DEFAULT_REGION"), DEFAULT_REGION, ] for regi
  • ault_aws_region(): return os.environ.get("AWS_DEFAULT_REGION") @lru_cache(maxsize=1) def get_sagemaker_i
  • account_id(): accountId = os.environ.get("AWS_ACCOUNT_ID") if accountId is None: # we are in s
  • alue old_aws_account_id = os.getenv("AWS_ACCOUNT_ID") # Update environment os.environ["AWS_AC
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Amazon" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with amazon-sagemaker-jupyter-scheduler 
Develop a small but impactful project named 'SageNote' which leverages the 'amazon-sagemaker-jupyter-scheduler' package to enhance the functionality of Jupyter Notebooks within the Amazon SageMaker environment. SageNote aims to streamline the execution of long-running tasks and resource-intensive computations by scheduling them efficiently. The project will include a user-friendly interface where users can input their Jupyter notebook cells or scripts and schedule them to run at specific times or intervals. Additionally, it will provide real-time status updates and results upon completion. Key features of SageNote include:

1. **Task Scheduling**: Users should be able to specify when and how often a task runs, including setting up recurring schedules.
2. **Resource Management**: Efficiently manage AWS resources such as EC2 instances and SageMaker notebooks to ensure cost-effectiveness and optimal performance.
3. **Status Tracking**: Provide a dashboard where users can monitor the status of their scheduled tasks, view logs, and get notified about any errors or successful completions.
4. **Result Retrieval**: Automatically save and retrieve results from executed tasks, making it easy for users to access outcomes without manually downloading files.
5. **User Interface**: Design a simple yet effective web-based UI that integrates seamlessly with Amazon SageMaker and allows users to interact with their scheduled tasks effortlessly.

The 'amazon-sagemaker-jupyter-scheduler' package will be central to this project, facilitating the scheduling and management of Jupyter tasks. It will enable the creation of a robust backend that handles task execution according to user-defined schedules, leveraging the power of Amazon SageMaker for scalable computing.

💬 Discussion Feed

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