axor-classifier-simple

v0.2.1 suspicious
7.0
High Risk

ML classifiers for axor-core: TaskSignalClassifier + MLAnomalyDetector

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential obfuscation and low-effort metadata, raising concerns about its legitimacy. While there's no direct evidence of malicious intent, these indicators suggest caution.

  • obfuscation risk due to hard-coded eval patterns
  • low-effort and suspicious metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
  • Obfuscation: The presence of specific hard-coded eval patterns suggests potential obfuscation for hiding code logic, which is suspicious but not definitively malicious.
  • Credentials: No clear evidence of credential harvesting was found, reducing the risk to a low level.
  • Metadata: The package shows signs of low effort and possibly suspicious author behavior, raising concerns about its legitimacy.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. test_anomaly_detector.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7259 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

  • 72 type-annotated function signatures detected in source
○ 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 score 6.0

Found 3 obfuscation pattern(s)

  • # ── support-specific hard eval (infra/k8s vocabulary) ───────────────────────── ("focuse
  • # ── analysis-specific hard eval (BI/SQL vocabulary) ─────────────────────────── ("focused
  • # ── general-specific hard eval (doc_type/work_product vocabulary) ────────────── ("focus
✓ 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 package
  • Author name is missing or very short
  • Author "" 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-simple 
Create a mini-application called 'TaskMonitor' using the Python package 'axor-classifier-simple'. This application will monitor system tasks and detect anomalies in task signals, helping users identify unusual patterns or potential issues in their system's task performance. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Ensure you have Python installed and create a virtual environment. Install the necessary packages including 'axor-classifier-simple', 'pandas', and 'matplotlib'.

2. **Data Collection**: Implement functionality to collect data from system tasks. This could include CPU usage, memory consumption, disk I/O operations, etc. For simplicity, you may simulate this data using random generators.

3. **Preprocessing**: Preprocess the collected data to make it suitable for analysis. This might involve normalizing values, handling missing data, and formatting timestamps.

4. **Classification**: Utilize the 'TaskSignalClassifier' from 'axor-classifier-simple' to classify different types of task signals. Train the classifier with your dataset and evaluate its performance.

5. **Anomaly Detection**: Use the 'MLAnomalyDetector' from 'axor-classifier-simple' to detect anomalies within the task signals. Identify which signals deviate significantly from the norm and flag them as potential issues.

6. **Visualization**: Create visual representations of the task signals and anomalies using 'matplotlib'. This helps users easily understand the patterns and outliers in the data.

7. **Alert System**: Develop an alert system that notifies the user when anomalies are detected. This could be via email, SMS, or a simple GUI notification.

8. **User Interface**: Design a basic command-line interface (CLI) for interacting with the application. Users should be able to start monitoring, view current status, and access historical data.

9. **Documentation**: Write comprehensive documentation explaining how to install and use the application, along with examples and troubleshooting tips.

10. **Testing**: Conduct thorough testing to ensure the application works as expected under various conditions. Include unit tests for each component of the application.

By following these steps, you'll create a powerful tool for monitoring system tasks and detecting anomalies, leveraging the capabilities of 'axor-classifier-simple'.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!