avenir-spectrum-export-pjnz

v0.1.2 suspicious
5.0
Medium Risk

PJNZ export for Spectrum Engine

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to network activities that could potentially involve data exfiltration or unauthorized communication, despite showing no signs of direct system compromise, obfuscation, or credential harvesting.

  • High network risk due to multiple POST requests
  • Low maintainer activity and poor metadata quality
Per-check LLM notes
  • Network: Multiple POST requests indicate potential data transmission which could be benign but may also suggest data exfiltration or unauthorized communication.
  • Shell: No shell execution patterns detected, indicating low risk for direct system compromise.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, which may indicate a lack of transparency or malicious intent.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (262 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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 9.0

Found 6 network call pattern(s)

  • # } # loginResponse = requests.post(url, json=loginData) createProjResponse = doServiceGetCa
  • } # createProjResponse = requests.post(url, json=createProjData) saveProjResponse = doServiceG
  • # } # saveProjResponse = requests.post(url, json=saveProjData) downloadProjResponse = doServi
  • # downloadProjResponse = requests.post(url, json=downloadProjData) path = json.loads(json.load
  • "result" ] file = requests.get(url + "/" + path) buffer = BytesIO(file.content) r
  • sword='+password response = requests.post(url_service, data=payload, headers=headers) if response.st
✓ 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

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 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with avenir-spectrum-export-pjnz 
Your task is to develop a mini-application called 'Spectrum Data Visualizer' using Python and the package 'avenir-spectrum-export-pjnz'. This application will serve as a tool for users to visualize and analyze data exported from the Spectrum Engine using the specified package. The application should include the following features:

1. **Data Import**: Allow users to import data exported from the Spectrum Engine using the 'avenir-spectrum-export-pjnz' package. Ensure that the application supports different file formats that the package can handle.
2. **Data Visualization**: Implement various visualization options such as line charts, bar graphs, and scatter plots to help users understand the imported data better. Use libraries like Matplotlib or Plotly for these visualizations.
3. **Interactive Exploration**: Provide interactive features where users can zoom in/out, pan across the graph, and select specific data points for more detailed analysis.
4. **Analysis Tools**: Include basic statistical analysis tools such as mean, median, mode, and standard deviation calculations for the imported data.
5. **Customization Options**: Allow users to customize the look of their graphs, including color schemes, axis labels, and titles.
6. **Export Functionality**: Enable users to export their visualizations as images or PDFs for reports or presentations.
7. **User Interface**: Develop a simple yet intuitive graphical user interface using a library like Tkinter or PyQt for the desktop version of the application.

The core functionality of your application will heavily rely on the 'avenir-spectrum-export-pjnz' package to ensure seamless data import and processing. Your goal is to create a versatile and user-friendly tool that leverages the capabilities of this package to provide valuable insights into the data exported from the Spectrum Engine.

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