AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risk in terms of network usage, shell execution, and code obfuscation. However, the metadata risk score is elevated due to the maintainer having only one package on PyPI, which raises some suspicion.
- Low network and shell execution risks
- No signs of code obfuscation or credential harvesting
- Elevated metadata risk due to single package by maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell executions detected, reducing the risk of unauthorized system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
- Metadata: The maintainer has only one package on PyPI, which might indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
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
Email domain looks legitimate: swarmauri.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository swarmauri/swarmauri-sdk appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Jacob Stewart" 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 EmbedXMP
Create a Python-based mini-application named 'MetadataManager' that leverages the EmbedXMP package to manage metadata within digital files efficiently. This application will serve as a powerful tool for photographers, graphic designers, and content creators who need to embed, read, and remove metadata from their images and documents. ### Features: 1. **User Interface**: Develop a simple yet intuitive command-line interface (CLI) where users can interact with the application using clear commands. 2. **Embed Metadata**: Allow users to embed XMP metadata into image files (JPEG, PNG, TIFF, etc.) and document files (PDF, DOCX, etc.). Users should be able to specify the type of metadata they want to embed (e.g., copyright information, creation date). 3. **Read Metadata**: Provide functionality to read and display all embedded XMP metadata from a file. Users should be able to see a structured view of the metadata in a readable format. 4. **Remove Metadata**: Implement a feature that allows users to selectively remove specific pieces of metadata from a file or completely clear all metadata. 5. **Batch Processing**: Enable batch processing capabilities so users can apply the same operation (embed, read, remove) on multiple files at once. 6. **Help and Documentation**: Include a help menu that explains each command and provides examples of how to use them effectively. 7. **Logging**: Add logging capabilities to track operations performed by the application, including errors and successes. ### How EmbedXMP is Utilized: - **Embedding Metadata**: Use EmbedXMP to dynamically embed metadata into files. For example, when embedding copyright information, use the appropriate EmbedXMP functions to create and attach the metadata to the file. - **Reading Metadata**: Employ EmbedXMP's reading capabilities to extract metadata from files. Display the extracted data in a user-friendly manner, such as printing it to the console or saving it to a text file. - **Removing Metadata**: Leverage EmbedXMP's removal functionalities to delete specific metadata entries from files. Ensure users can choose which metadata to remove and confirm the action before proceeding. - **Batch Processing**: Extend the application to handle multiple files simultaneously. For each file, apply the specified operation using EmbedXMP and ensure the process is efficient and error-free. This project aims to provide a robust solution for managing metadata within digital files, enhancing privacy, security, and compliance for users.