AI Analysis
Final verdict: SUSPICIOUS
The package shows some level of risk due to potential external resource downloads and concerns over repository activity and author history, though it does not exhibit any direct signs of malicious behavior.
- network risk due to external resource downloads
- low repository activity and author history
Per-check LLM notes
- Network: The network call suggests the package may be downloading external resources which could be related to its functionality, but requires further investigation to confirm legitimacy.
- Shell: No shell execution patterns were detected, indicating low risk in this area.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
- Metadata: The low activity in the repository and the author's limited history suggest potential unreliability, but there are no clear signs of malicious intent.
Package Quality Overall: Low (4.6/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (2241 chars)
○ 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
40 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
5 unique contributor(s) across 81 commits in savaij/astrodetectionActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
locally...") r = requests.get( "https://dl.fbaipublicfiles.com/fasttext/su
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: proton.me
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Francesco Savatteri" 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 astrodetection
Create a social media analysis tool using Python that identifies potential astroturfing activities on platforms like Twitter. This mini-application should be designed to help users and organizations detect coordinated inauthentic behaviors which might indicate astroturfing campaigns. Here’s a detailed plan for building this tool: 1. **Project Setup**: Start by setting up your development environment. Ensure you have Python installed along with the necessary libraries such as `astrodetection`, `tweepy` for Twitter API access, and `pandas` for data manipulation. 2. **Twitter Data Collection**: Use Tweepy to collect tweets based on specific hashtags or keywords relevant to your analysis. Consider collecting tweets over a period of time to analyze trends and patterns. 3. **Data Preprocessing**: Clean the collected data by removing retweets, duplicates, and irrelevant content. Normalize text data for better analysis. 4. **Astroturfing Detection**: Utilize the `astrodetection` package to analyze the preprocessed data. Integrate its core functionalities to identify signs of astroturfing such as unusual posting patterns, similar tweet structures, and rapid growth of followers among accounts. 5. **Visualization**: Implement visualization tools to present the findings in an understandable manner. Use libraries like Matplotlib or Seaborn to create graphs and charts that highlight suspicious activity. 6. **Report Generation**: Develop a feature to generate detailed reports summarizing the analysis. Include insights into detected anomalies, potential astroturfing activities, and recommendations. 7. **User Interface**: Optionally, create a simple web interface using Flask or Django where users can input their queries (hashtags, keywords), view real-time analytics, and download reports. 8. **Testing and Validation**: Test the application thoroughly with known datasets to validate its accuracy in detecting astroturfing activities. Adjust parameters and models as necessary to improve performance. 9. **Documentation**: Write comprehensive documentation explaining how to use the tool, including setup instructions, usage examples, and best practices. By following these steps, you will develop a powerful yet user-friendly tool that leverages the capabilities of `astrodetection` to combat astroturfing on social media.
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