PAWpy

v0.1.0 suspicious
5.0
Medium Risk

A TM1py-inspired Python wrapper for the IBM Planning Analytics Workspace (PAW) REST API.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity, but its extremely new and inactive repository raises concerns about its legitimacy and purpose.

  • Metadata risk due to new and minimally active repository
  • No immediate signs of network, shell, obfuscation, or credential risks
Per-check LLM notes
  • Network: The use of requests.Session() with optional proxy support suggests the package may perform network requests, which is not inherently suspicious but should be reviewed to understand the context and necessity.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository and package are extremely new with minimal activity, indicating potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • {}) self._session = requests.Session() self._session.verify = verify if proxies:
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: hotmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 10.0

Git history flags: Repository created very recently: 2 day(s) ago (2026-06-04T04:42:18Z)

  • Repository created very recently: 2 day(s) ago (2026-06-04T04:42:18Z)
  • Repository has zero stars and zero forks
  • Single contributor with only 3 commit(s) — possibly throwaway account
  • All 3 commits happened within 24 hours
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Package is very new: uploaded 1 day(s) ago
  • 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 PAWpy 
Create a fully-functional mini-application named 'PAWpyDashboard' using the PAWpy Python package. This application will serve as a dashboard tool for users to interact with their IBM Planning Analytics Workspace (PAW) environment, providing them with real-time insights and data manipulation capabilities.

Step 1: Setup the Environment
- Install Python and ensure you have pip installed.
- Use pip to install the PAWpy package from PyPI.
- Set up your IBM PAW credentials and any necessary configuration files.

Step 2: Define Core Features
- **Data Retrieval**: Implement functionality to fetch data from different cubes within the PAW environment.
- **Data Manipulation**: Allow users to perform basic operations like addition, subtraction, and aggregation on fetched data.
- **Visualization**: Integrate a simple charting library (such as matplotlib or seaborn) to visualize the retrieved data.
- **User Interface**: Develop a user-friendly interface using a web framework such as Flask or Django. This UI should allow users to select which cube they want to interact with, perform operations, and view visualizations.

Step 3: Utilize PAWpy
- Use PAWpy's methods to connect to your PAW environment securely.
- Leverage PAWpy's capabilities to query data from selected cubes efficiently.
- Implement error handling to manage potential issues during data retrieval and processing.

Step 4: Testing and Deployment
- Test your application thoroughly under various scenarios to ensure reliability.
- Deploy your application to a cloud service provider of your choice, ensuring secure access and scalability.

Additional Suggested Features:
- **Permissions Management**: Allow administrators to control who can access specific cubes.
- **Scheduled Data Refresh**: Enable users to schedule periodic updates to ensure the dashboard always reflects the latest data.
- **Custom Queries**: Provide an option for advanced users to write custom queries using PAWpy's API.

By following these steps and utilizing PAWpy effectively, you'll create a powerful tool that simplifies interaction with IBM PAW environments.