AI Analysis
Final verdict: SUSPICIOUS
The package exhibits low risks in terms of network, shell execution, and obfuscation. However, metadata concerns, such as a non-HTTPS link and a new maintainer with limited history, elevate the risk level slightly.
- Non-HTTPS link in package metadata
- New maintainer with only one package
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network functionality.
- Shell: No shell execution detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting no immediate threat to stored secrets or credentials.
- Metadata: The package shows some potential red flags, including a non-HTTPS link and a new maintainer with only one package, but there's no strong 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
score 3.0
Possible typosquat of: moto
"MFTE" is 2 edit(s) from "moto"
Registered Email Domain
Email domain looks legitimate: outlook.com
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://sites.google.com/site/multidimensionaltagger
Git Repository History
Repository mshakirDr/MFTE appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Muhammad Shakir" 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 MFTE
Create a Python-based mini-application called 'TextProfiler' that leverages the MFTE package to analyze and profile English texts. The application should allow users to input a text file or paste text directly into a user interface (UI) to get a comprehensive analysis of various linguistic features such as syntactic, semantic, and pragmatic characteristics. The goal is to provide insights into the stylistic and structural nuances of the text, which could be useful for linguists, writers, and researchers. ### Key Features: - **Text Input:** Users can either upload a text file or paste text directly into the application. - **Linguistic Analysis:** Utilize the MFTE package to tag and categorize the text according to its syntactic and semantic features as per Biber's framework. - **Interactive UI:** Develop a simple, intuitive web interface using Flask or Django where users can interact with the application. - **Visualization:** Display the analysis results in a visually appealing manner, such as bar charts or pie charts, showing the distribution of different linguistic features. - **Export Options:** Allow users to export the analysis results in formats like PDF or CSV. ### Steps to Build the Application: 1. **Set Up the Environment:** Ensure you have Python installed along with the necessary libraries (Flask/Django, matplotlib for visualization, etc.). Install the MFTE package. 2. **Develop the Backend Logic:** Write functions that take text input, process it through the MFTE package, and return the tagged data. 3. **Design the Frontend:** Create a user-friendly web interface using HTML/CSS/JavaScript, or use pre-built templates provided by Flask/Django. 4. **Integrate Visualization:** Use matplotlib or any other library to visualize the linguistic features identified by MFTE. 5. **Implement Export Functionality:** Add options for exporting the analysis in PDF or CSV format. 6. **Testing and Debugging:** Thoroughly test the application to ensure all features work as expected and fix any bugs encountered. 7. **Deployment:** Deploy the application on a cloud service like Heroku or AWS so that it can be accessed online. ### How to Utilize MFTE: - Import the MFTE package and use its tagging functions to analyze the text. - Customize the tagging process if needed to fit specific requirements of the TextProfiler application. - Extract the tagged data and prepare it for visualization and reporting.