AI Analysis
Final verdict: SAFE
The PySastrawi package is assessed as safe based on its low risk scores across all categories except metadata, where it shows a moderate score due to the maintainer's limited package history.
- No network calls or shell executions detected
- Low obfuscation and credential risk
- Moderate metadata risk due to single package from maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal for a text processing library like PySastrawi.
- Shell: No shell executions detected, aligning with the expected behavior of a text processing library.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but there are no other suspicious flags.
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: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository har07/PySastrawi appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Hanif Amal Robbani" 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 PySastrawi
Create a web-based text analysis tool using Flask and PySastrawi that helps users analyze the readability and sentiment of Bahasa Indonesia texts. The application should allow users to input any Bahasa Indonesia text and receive an output that includes: 1. Stemmed words from the text, which will help in understanding the root form of the words. 2. A word frequency chart showing the most common words in the text, excluding common stop words. 3. An assessment of the text's readability based on specific metrics such as average sentence length and average number of syllables per word. 4. Sentiment analysis, providing a score indicating whether the overall sentiment of the text is positive, negative, or neutral. The user interface should be simple and intuitive, allowing easy text input and displaying results in a clear, organized manner. Additionally, include a feature where users can save their analyses and view them later. To achieve this, you will utilize PySastrawi for stemming the text, NLTK for handling stop words and performing basic text analysis, and TextBlob or another sentiment analysis library for sentiment scoring. Ensure your code is well-documented and includes comments explaining each significant part of the process.