arrow-odbc

v10.4.1 suspicious
5.0
Medium Risk

Read the data of an ODBC data source as sequence of Apache Arrow record batches.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to shell execution commands observed, which could potentially lead to unauthorized database manipulations. However, there are no signs of obfuscation, network risks, or credential theft.

  • Shell risk 8/10 due to observed database table manipulation commands
  • Low risk in other areas with no clear malicious intent
Per-check LLM notes
  • Network: No network calls detected, minimal risk.
  • Shell: Shell execution commands observed that manipulate database tables, indicating potential unauthorized operations which could be risky.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The author's new or inactive account and lack of PyPI classifiers suggest low effort, but there are no clear indicators of malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: conftest.py
  • 2 test file(s) detected (e.g. test_arrow_odbc.py)
◈ Medium Documentation 7.0

Some documentation present

  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (9936 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

  • 19 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in pacman82/arrow-odbc-py
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • """ table = "Iris" os.system(f'odbcsv fetch -c "{MSSQL}" -q "DROP TABLE IF EXISTS {table}
  • BLE IF EXISTS {table};"') os.system( f'odbcsv fetch -c "{MSSQL}" -q "CREATE TABLE {table
  • ety VARCHAR(20) )"' ) os.system(f'odbcsv insert -c "{MSSQL}" -i ./tests/iris.csv {table}')
  • ble = "InsertLargeString" os.system(f'odbcsv fetch -c "{MSSQL}" -q "DROP TABLE IF EXISTS {table}
  • BLE IF EXISTS {table};"') os.system(f'odbcsv fetch -c "{MSSQL}" -q "CREATE TABLE {table} (a NVAR
  • otLeakMemoryForEachBatch" os.system(f'odbcsv fetch -c "{MSSQL}" -q "DROP TABLE IF EXISTS {table}
✓ 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

Repository pacman82/arrow-odbc-py appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Markus Klein" 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 arrow-odbc 
Develop a Python-based mini-application that allows users to interact with an ODBC-compatible database using the 'arrow-odbc' package. This application will serve as a simple yet powerful tool for data analysis and manipulation, providing real-time insights from databases such as SQL Server, MySQL, or Oracle.

### Application Features:
1. **Database Connection**: Allow users to connect to their ODBC data sources through a user-friendly interface. This includes specifying connection details like server name, port, username, password, etc.
2. **Query Execution**: Enable users to execute SQL queries against the connected database. The results should be displayed in a tabular format, making it easy for users to understand and analyze the data.
3. **Data Visualization**: Integrate basic visualization capabilities to help users visualize query results. This could include charts, graphs, or other visual representations of the data.
4. **Export Functionality**: Provide options to export the query results into common file formats like CSV, Excel, or PDF.
5. **Performance Metrics**: Display performance metrics such as execution time and number of rows processed for each query.

### Utilizing 'arrow-odbc':
- Use the 'arrow-odbc' package to read data from the ODBC data source as sequences of Apache Arrow record batches. This will enable efficient handling of large datasets.
- Leverage Apache Arrow's capabilities for in-memory data processing, which can significantly enhance the speed and efficiency of your application.
- Explore advanced functionalities provided by 'arrow-odbc', such as filtering, sorting, and aggregating data directly within the Arrow record batches before displaying them to the user.

### Development Steps:
1. Set up a Python environment with all necessary packages installed, including 'arrow-odbc'.
2. Design the user interface for connecting to the database, executing queries, and viewing results.
3. Implement the backend logic for handling connections, queries, and data processing using 'arrow-odbc'.
4. Add visualization components to represent the data visually.
5. Implement the export functionality for different file types.
6. Test the application thoroughly with various databases and queries to ensure reliability and performance.
7. Document the application, including setup instructions, usage guidelines, and any known limitations.

This project not only leverages the power of 'arrow-odbc' for efficient data handling but also provides a practical tool for users to interact with their databases in a more intuitive way.

💬 Discussion Feed

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