AI Analysis
Final verdict: SUSPICIOUS
The package shows some signs of obfuscation and potential metadata issues, but lacks clear indicators of malicious intent or active threats like network risks or shell execution.
- Obfuscation risk due to pickle.loads usage
- Repository not found and possible inactive maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized system access.
- Obfuscation: The use of pickle.loads and obfuscation techniques suggests potential risk as it can be used for hiding malicious code.
- Credentials: No clear signs of credential harvesting detected.
- Metadata: The package has no typosquatting, email domain, or suspicious links flags, but the repository is not found and the maintainer history indicates potential inactivity or a new account.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
Exit(code=1) task_list = pickle.loads(tasks.read_bytes()) backend = LocalProcessBackend(max_w
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: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 aereo
Create a Python-based mini-application named 'SatelliteDataAnalyzer' that leverages the 'aereo' package to provide users with an intuitive interface for searching, extracting, and analyzing satellite imagery data. This application will serve as a tool for environmental scientists, geographers, and researchers who need quick access to satellite data without needing extensive programming knowledge. Step 1: Setup Environment - Install Python and necessary libraries including 'aereo'. - Set up a virtual environment for the project. Step 2: Design User Interface - Develop a simple command-line interface (CLI) that allows users to interact with the application. - Implement a basic GUI using a library like PyQt or Tkinter for more advanced users. Step 3: Integration of Aereo Package - Utilize 'aereo' to connect to various satellite data catalogs. - Implement functionalities within the application to allow users to search for specific satellite images based on date, location, and type. Step 4: Data Extraction and Processing - Enable users to select and download satellite images directly from the application. - Use 'aereo' to automatically reproject the downloaded data into analysis-ready Major TOM grids. - Integrate image processing capabilities to perform basic analyses such as calculating NDVI (Normalized Difference Vegetation Index). Step 5: Visualization - Provide options for visualizing the processed satellite images and analysis results through the application. - Allow users to export visualizations as high-quality images or PDFs. Suggested Features: - User authentication to track usage and manage access levels. - Support for batch processing of multiple satellite images. - Integration with cloud storage services for easy data sharing and backup. - Advanced filtering options based on metadata associated with the satellite images.