aiq-magnet

v0.0.2 suspicious
5.0
Medium Risk

Kitware's MAGNET framework for AIQ

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation and some metadata concerns, raising suspicion about its true intentions despite no direct evidence of malicious activity.

  • High obfuscation risk
  • Concerning metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell executions detected, indicating the package does not execute system commands.
  • Obfuscation: The code shows signs of deliberate obfuscation which could be used to hide malicious activities or make analysis difficult.
  • Credentials: No clear patterns of credential harvesting were detected in the provided code snippet.
  • Metadata: Low risk due to lack of suspicious flags, but concerns over author details and package maintenance suggest potential low effort or new maintainer.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. test_import.py)
○ 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
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 143 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 7 unique contributor(s) across 100 commits in AIQ-Kitware/aiq-magnet
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • ': "x = [10]"}) >>> x.eval() [10] """ def __init__(self, name, spec):
  • et('depends_on', []) def eval(self, context: Dict[str, Any] = {}) -> Any: """
  • e, 'Any': Any} type = eval(type_str, str_to_type) return self._check_collection
  • itions[symbol] = symbol_value.eval( symbol_definitions_ )
✓ 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: kitware.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository AIQ-Kitware/aiq-magnet appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • 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 aiq-magnet 
Your task is to develop a Python-based mini-application that leverages the 'aiq-magnet' package to perform advanced data analysis and visualization on a dataset of your choice. This application will serve as a tool for researchers and data scientists to explore complex datasets using the powerful AIQ (Artificial Intelligence Quality) metrics provided by the MAGNET framework.

Step 1: Choose a dataset relevant to your field of interest (e.g., financial data, medical records, environmental data). Ensure the dataset is publicly available and accessible via APIs or downloadable files.

Step 2: Install the 'aiq-magnet' package and any other necessary dependencies. Familiarize yourself with the documentation and examples provided by the package to understand its capabilities and limitations.

Step 3: Design the user interface of your application. It should be intuitive and easy to navigate, allowing users to upload their own datasets or select from preloaded options. Consider implementing a dashboard-like layout with multiple tabs or sections for different functionalities.

Step 4: Implement the core functionality of your application. Utilize the 'aiq-magnet' package to perform quality assessments on the selected dataset. This could include analyzing the integrity, consistency, and accuracy of the data. Display the results in a clear and concise manner, possibly through graphs, charts, or tables.

Step 5: Enhance your application by adding additional features such as:
- Data preprocessing tools (cleaning, normalization)
- Advanced statistical analyses (regression, clustering)
- Interactive visualizations (dynamic charts, heatmaps)
- Export options for results (CSV, PDF)

Step 6: Test your application thoroughly with various datasets to ensure reliability and robustness. Address any bugs or issues that arise during testing.

Step 7: Document your application's usage and features comprehensively. Include instructions on installation, configuration, and operation. Provide examples and use cases to help new users get started quickly.

By following these steps, you'll create a valuable tool that showcases the power of 'aiq-magnet' while providing practical benefits to real-world data scientists and researchers.

💬 Discussion Feed

Leave a comment

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