AI Analysis
Final verdict: SUSPICIOUS
The package has low risks in terms of network usage, shell execution, obfuscation, and credential harvesting. However, the incomplete metadata and lack of maintainer history raise suspicion, suggesting potential issues with the package's provenance.
- Incomplete metadata
- Lack of maintainer history
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package appears suspicious due to lack of maintainer history and incomplete metadata.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 2 test file(s) found
Test runner config found: pyproject.toml2 test file(s) detected (e.g. test_verifier.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (5367 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
7 type-annotated function signatures (partial)
○ 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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 8.0
4 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with axor-classifier-llm
Create a fully-functional mini-application called 'AnomalyGuard' using the Python package 'axor-classifier-llm'. AnomalyGuard will serve as an advanced anomaly detection tool designed to identify potential anomalies in real-time data streams, such as network traffic, financial transactions, or IoT sensor readings. The application will leverage the 'axor-classifier-llm' package to verify anomalies detected by the 'axor-core' system, providing a more nuanced understanding of whether these anomalies fall into a 'gray zone' where they are neither clearly normal nor clearly malicious. Step 1: Set Up Your Development Environment - Ensure you have Python installed (version 3.7 or higher). - Install necessary packages including 'axor-classifier-llm', 'pandas', and 'numpy'. - Configure your development environment (IDE or text editor) to work comfortably with Python. Step 2: Design the Data Pipeline - Implement a function to ingest real-time data from a chosen source (e.g., CSV files, database queries, or API calls). - Preprocess the data to ensure it is clean and formatted correctly for anomaly detection. Step 3: Integrate axor-classifier-llm - Use the 'axor-classifier-llm' package to create a model capable of classifying anomalies detected by 'axor-core'. - Train the model on historical data that includes both normal and anomalous behaviors. - Validate the model's accuracy and adjust parameters as needed to improve performance. Step 4: Build Real-Time Anomaly Detection - Develop a real-time data processing pipeline that feeds data into the 'axor-core' anomaly detector. - Implement a verification step using 'axor-classifier-llm' to evaluate the likelihood of each detected anomaly being truly anomalous. - Display results in real-time, highlighting any anomalies that fall into the 'gray zone'. Step 5: Enhance User Interaction - Create a simple web interface using Flask or Django to visualize the real-time anomaly detection process. - Allow users to input custom thresholds for what constitutes an anomaly. - Provide options to export detailed reports on detected anomalies and their classification status. Suggested Features: - Support for multiple data sources and types. - Automated alerts for critical anomalies. - Historical trend analysis to better understand patterns over time. - Integration with external systems for automated response to confirmed anomalies. - Customizable models within 'axor-classifier-llm' to fit specific use cases.
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