AI Analysis
Final verdict: SUSPICIOUS
The package exhibits low risk for common attack vectors like network calls and shell execution, but its metadata raises concerns due to missing maintainer history and author details.
- Lack of maintainer history
- Missing author details
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access to function properly.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- Metadata: The package shows some red flags such as lack of maintainer history and missing author details, but there's no concrete evidence of malicious intent.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
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 packageAuthor name is missing or very shortAuthor "" 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 SeismicFlow
Develop a seismic data analysis tool using the Python package 'SeismicFlow'. This tool will be designed to help geophysicists and researchers analyze seismic data efficiently. Here’s a step-by-step guide on how to create this application: 1. **Project Setup**: Begin by setting up your Python environment. Ensure you have Python installed along with necessary libraries such as SeismicFlow, NumPy, Pandas, and Matplotlib. 2. **Data Importation**: Implement functionality to import seismic data from various file formats commonly used in geophysics, such as SEG-Y. Use SeismicFlow’s data handling capabilities to facilitate seamless integration of these datasets. 3. **Data Preprocessing**: Develop preprocessing routines within the app to clean and normalize the imported data. Utilize SeismicFlow’s preprocessing tools to filter out noise, correct for baseline shifts, and enhance signal clarity. 4. **Analysis Tools**: Integrate analysis modules that leverage SeismicFlow’s core functionalities. These could include frequency analysis, amplitude variation with offset (AVO) analysis, and velocity spectrum computation. Each module should provide visual outputs and statistical summaries to aid interpretation. 5. **Visualization Interface**: Create a user-friendly interface for visualizing seismic data. Use Matplotlib or Plotly to generate interactive plots and graphs that allow users to explore different aspects of the seismic data, such as time series, cross-sections, and 3D representations. 6. **Report Generation**: Add a feature that automatically generates comprehensive reports summarizing the analysis performed. Include sections on methodology, key findings, and recommendations based on the data analysis. 7. **User Documentation**: Write detailed documentation explaining how to use each feature of the application, including setup instructions, usage guidelines, and troubleshooting tips. 8. **Testing and Validation**: Conduct thorough testing of the application to ensure all components work as expected. Validate the results against known datasets to confirm accuracy and reliability. By following these steps, you’ll create a powerful and versatile seismic data analysis tool that leverages the advanced features of SeismicFlow.