analogic-framework

v4.1.103 suspicious
5.0
Medium Risk

(No description)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential network misuse via SMTP and low maintainer activity. While there is no concrete evidence of malicious intent, these factors combined warrant caution.

  • moderate network risk due to SMTP usage
  • low maintainer activity and poor metadata quality
Per-check LLM notes
  • Network: The use of SMTP suggests the package may be sending emails which could be part of its intended functionality, but requires further investigation to ensure it's not being used for unauthorized communications.
  • Shell: No shell execution patterns were detected, indicating a low risk of direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package shows low maintainer activity and poor metadata quality, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 16 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 1.5

Found 1 network call pattern(s)

  • s False: server = smtplib.SMTP(smtp_server, port) if is_tls: se
✓ 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 6.0

3 maintainer concern(s) found

  • 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 analogic-framework 
Create a mini-application called 'AnalogicSignalAnalyzer' using the Python package 'analogic-framework'. This application will serve as a basic tool for analyzing and visualizing analog signals, such as those from sensors or audio equipment. Here’s a detailed breakdown of the steps and features your project should include:

1. **Setup**: Begin by installing the 'analogic-framework' package if it isn't already installed. Ensure you have all necessary dependencies for data visualization and signal processing.
2. **User Interface**: Develop a simple yet intuitive command-line interface (CLI) where users can input their analog signal data either manually or through file uploads. The CLI should also allow users to select which analysis functions they wish to perform on their data.
3. **Data Input & Processing**: Implement functionality within 'AnalogicSignalAnalyzer' to accept various types of analog signal data inputs (e.g., time-domain signals). Utilize 'analogic-framework' to process these signals, including filtering out noise, smoothing the data, and converting signals between different domains (time to frequency, etc.).
4. **Analysis Functions**: Provide several key analysis options such as calculating signal power, identifying peaks and troughs, determining the signal-to-noise ratio (SNR), and more. Each function should leverage the capabilities of 'analogic-framework' to accurately perform its task.
5. **Visualization**: Incorporate a feature that allows users to visualize their processed signals along with any analysis results. Use libraries like Matplotlib or Plotly to create graphs that display the original signal, filtered signal, spectral content, and other relevant metrics.
6. **Output Options**: Enable users to save their analysis results and visualizations in formats like CSV for data, and PNG/JPEG for images. Additionally, provide an option to export a summary report detailing all performed analyses and findings.
7. **Documentation & Testing**: Finally, write comprehensive documentation explaining how to use 'AnalogicSignalAnalyzer', including examples and best practices. Conduct thorough testing to ensure all components work as expected and are robust against common errors.

By following these steps, you'll create a powerful yet accessible tool for anyone interested in exploring and understanding analog signals through the lens of 'analogic-framework'.

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