altertable-flightsql

v0.3.1 suspicious
4.0
Medium Risk

Python client library for Altertable

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risk due to potential obfuscation techniques that may be used for evading detection. While there are no direct signs of malicious intent, the lack of detailed maintainer information and potential inactivity add to the suspicion.

  • High obfuscation risk
  • Potential lack of maintainer activity
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 risk of command injection or similar attacks.
  • Obfuscation: The observed patterns likely indicate some form of data obfuscation or encoding, possibly for payload delivery or to evade simple detection mechanisms.
  • Credentials: No clear signs of credential harvesting were found.
  • Metadata: The package shows signs of potential inactivity and lack of maintainer information, raising some concerns but not definitive proof of malice.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. test_client.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/yourusername/altertable-flightsql#readme
  • Detailed PyPI description (5505 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 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 48 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • \x12\x1d\n\x10XDBC_LONGVARCHAR\x10\xff\xff\xff\xff\xff\xff\xff\xff\xff\x01\x12\x18\n\x0bXDBC_BINARY\x10\xfe\xff\xff\xff\xff\xff\xff\xff\xff\x0
  • f\x01\x12\x18\n\x0bXDBC_BINARY\x10\xfe\xff\xff\xff\xff\xff\xff\xff\xff\x01\x12\x1b\n\x0eXDBC_VARBINARY\x10\xfd\xff\xff\xff\xff\xff\xff\xff\xff
  • 01\x12\x1b\n\x0eXDBC_VARBINARY\x10\xfd\xff\xff\xff\xff\xff\xff\xff\xff\x01\x12\x1f\n\x12XDBC_LONGVARBINARY\x10\xfc\xff\xff\xff\xff\xff\xff\xff
  • 12\x1f\n\x12XDBC_LONGVARBINARY\x10\xfc\xff\xff\xff\xff\xff\xff\xff\xff\x01\x12\x18\n\x0bXDBC_BIGINT\x10\xfb\xff\xff\xff\xff\xff\xff\xff\xff\x0
  • f\x01\x12\x18\n\x0bXDBC_BIGINT\x10\xfb\xff\xff\xff\xff\xff\xff\xff\xff\x01\x12\x19\n\x0cXDBC_TINYINT\x10\xfa\xff\xff\xff\xff\xff\xff\xff\xff\x
  • \x01\x12\x19\n\x0cXDBC_TINYINT\x10\xfa\xff\xff\xff\xff\xff\xff\xff\xff\x01\x12\x15\n\x08XDBC_BIT\x10\xf9\xff\xff\xff\xff\xff\xff\xff\xff\x01\x
✓ 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: example.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 altertable-flightsql 
Create a Python-based flight data analysis tool named 'FlightInsight' using the 'altertable-flightsql' package. This tool will allow users to query real-time flight data from various airlines and airports, providing insights such as average delays, most frequent destinations, and busiest times of day. The application should have a user-friendly command-line interface (CLI) for inputting queries and displaying results.

Core Features:
1. Users should be able to connect to the Altertable service and authenticate their credentials.
2. Implement functions to fetch live flight data based on specific criteria such as airline, airport, date range, etc.
3. Provide an option to analyze historical flight data stored within the Altertable database.
4. Include visualizations (e.g., bar charts, pie charts) to represent key statistics about the queried data.
5. Allow saving of query results to local files in CSV format.
6. Integrate error handling to manage issues like invalid inputs or connection failures gracefully.

How to Utilize 'altertable-flightsql':
- Use the 'connect' method from the 'altertable-flightsql' package to establish a secure connection to the Altertable server.
- Leverage the 'query' function to execute SQL-like commands against the flight data stored in the database.
- Apply filters and transformations on the fetched data using Python's pandas library for more complex analysis.
- Ensure all sensitive information like API keys or passwords are handled securely, perhaps through environment variables or a configuration file.

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