AI Analysis
Final verdict: SAFE
The package appears to be safe with no indications of malicious activity. It has low risk scores across all categories analyzed.
- No network calls
- No shell execution
- No obfuscation
- No credential harvesting
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network interactions.
- Shell: No shell execution patterns detected, indicating no direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting the package does not engage in unauthorized secret or credential collection.
Package Quality Overall: Low (1.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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: baseinformation.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Proteus Technology PVT. LTD." 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 applicationutility
Create a Python-based log sanitization tool named 'LogSanitizer' using the 'applicationutility' package. This tool will help developers and system administrators clean their log files by removing sensitive information such as API keys, database credentials, and other confidential data before sharing them for debugging purposes or archiving. Step 1: Install the necessary packages including 'applicationutility'. Step 2: Design a user-friendly command-line interface where users can specify the input log file and output sanitized log file. Step 3: Implement functionality to detect and remove known patterns of sensitive data like OpenAI keys, Gemini API keys, and database credentials. Step 4: Allow customization of the sanitization rules via a configuration file or command-line arguments for more flexibility. Step 5: Ensure that the tool provides a summary of the changes made after sanitization, highlighting the removed data types. Features: - Automatic detection of common sensitive data patterns. - Customizable sanitization rules. - Detailed summary of sanitization actions. - Support for multiple log formats (text, json). - Option to save the sanitized logs to a new file or overwrite the original. The 'applicationutility' package will be primarily used for its core feature of removing sensitive information from text inputs, which can be adapted to work on log files. Specifically, it will help in identifying and obfuscating sensitive data according to predefined criteria.
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