aurora-cycler-manager

v0.11.4 suspicious
6.0
Medium Risk

Battery cycler management, data analysis and visualisation.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a notable level of obfuscation and potential credential handling, raising concerns about its true intentions and the security of stored credentials.

  • High obfuscation risk due to base64 decoding
  • Potential credential harvesting due to checks in standard locations
Per-check LLM notes
  • Network: The use of SSH client indicates network connectivity to remote servers, which is not inherently malicious but requires scrutiny to ensure proper authorization and secure handling of credentials.
  • Shell: No shell execution patterns detected, indicating low risk for direct command execution on local systems.
  • Obfuscation: Base64 decoding of content strings may indicate an attempt to hide or protect sensitive information within the package.
  • Credentials: Checking for credentials in standard locations such as '~/.ssh/id_rsa' suggests the package might be designed to access or manage SSH keys, which poses a high risk of credential harvesting.
  • Metadata: The package has no associated GitHub repository and the author information is incomplete, which raises some suspicion but does not conclusively indicate malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 17 test file(s) found

  • Test runner config found: conftest.py
  • 17 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4993 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

  • 387 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 score 3.0

Found 2 network call pattern(s)

  • return None jump = paramiko.SSHClient() jump.load_system_host_keys() jump.connect(
  • ion.""" self.client = paramiko.SSHClient() self.client.load_host_keys(CONFIG["SSH known hosts
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • .split(",") decoded = base64.b64decode(content_string) excel_file = pd.ExcelFile(io.BytesIO
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • t in standard location (e.g. '~/.ssh/id_rsa'). 'SSH known hosts path': Path to the SSH known ho
✓ 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 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aurora-cycler-manager 
Create a battery health monitoring application using the Python package 'aurora-cycler-manager'. This application will serve as a tool for researchers and engineers to manage, analyze, and visualize data from battery cyclers. Here’s a step-by-step guide on how to build it:

1. **Project Setup**: Begin by setting up your Python environment and installing the necessary packages, including 'aurora-cycler-manager'. Ensure you have a clean virtual environment for this project.
2. **Data Collection Interface**: Develop a user-friendly interface where users can upload or input data from their battery cycler tests. This could be CSV files or direct database entries. Use 'aurora-cycler-manager' to handle the initial data loading and preprocessing steps.
3. **Data Management Module**: Implement a module that allows users to manage their datasets. They should be able to view, edit, delete, and filter datasets based on specific criteria such as test date, battery type, etc. Utilize 'aurora-cycler-manager' functionalities for efficient data handling.
4. **Analysis Tools**: Build tools within the app to perform various analyses on the battery cycle data. Include options for calculating key metrics like cycle life, capacity retention, and efficiency. 'aurora-cycler-manager' provides powerful data analysis capabilities that should be leveraged here.
5. **Visualization Dashboard**: Create an interactive dashboard that visualizes the analyzed data through graphs and charts. Users should be able to select which metrics to display and customize the visualization settings. Leverage 'aurora-cycler-manager’ for generating these visualizations.
6. **Report Generation**: Integrate a feature that generates comprehensive reports based on the selected data and analysis. These reports should include all relevant metrics, visualizations, and any notes or comments added by the user during data entry.
7. **User Authentication & Security**: Ensure the application includes basic security measures such as user authentication and secure data storage. Only authenticated users should be able to access and modify their datasets.
8. **Documentation & User Guide**: Provide thorough documentation and a user guide that explains how to use each feature of the application effectively. Highlight how 'aurora-cycler-manager' enhances the functionality and ease-of-use of the application.

Suggested Features:
- Support for multiple data formats (CSV, JSON, SQL databases).
- Real-time data streaming from connected battery cyclers.
- Advanced filtering options for dataset management.
- Customizable alert system for significant changes in battery performance.
- Integration with cloud storage services for backup and sharing.

By following these steps and utilizing the full potential of 'aurora-cycler-manager', you will create a robust and versatile tool for managing and understanding battery cycle data.

💬 Discussion Feed

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