AI Analysis
Final verdict: SAFE
The package appears to be legitimate with no clear signs of malicious intent. The observed risks are minimal and common in many packages.
- No network calls detected.
- Unconventional import patterns but no strong evidence of obfuscation.
Per-check LLM notes
- Network: No network calls detected, which is typical and safe.
- Shell: The use of os.system and subprocess.call indicates shell execution, but without suspicious commands or network activities, it's likely for package-specific tasks rather than malicious intent.
- Obfuscation: The code uses unconventional import patterns which may indicate an attempt to obscure the true purpose of the imports, but it does not strongly suggest malicious intent.
- Credentials: No suspicious patterns indicative of credential harvesting were found in the provided code snippet.
- Metadata: The author has only one package, which may indicate a new or less active account, but no other suspicious elements were found.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 10.0
Found 6 obfuscation pattern(s)
port os import sys try: __import__('pylibmc') except ImportError: print("Mocking pylibmc for docs")if '.' in module: m = __import__('relstorage.%s' % module, fromlist=['chicken']) else: m = __import__('relstorage.%s' % module)cken']) else: m = __import__('relstorage.%s' % module) m = getattr(m, module) title = getattr(m, '__doeMVCCDatabaseViewer logger = __import__('logging').getLogger(__name__) @interface.implementer(IDetachableMVCCckRowsDeadlockError logger = __import__('logging').getLogger(__name__) class AbstractAdapter(DatabaseHelpersM. import interfaces logger = __import__('logging').getLogger(__name__) __all__ = [ 'LoadConnection',
Shell / Subprocess Execution
score 4.0
Found 2 shell execution pattern(s)
nviron.get('PYTHONPATH', '') os.system('%s generate_rst.py generate' % sys.executable) extensionsrement) import subprocess if subprocess.call(cmd) != 0: raise Exception( "Failed to execute c
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: willowrise.com
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://gevent.org
Git Repository History
Repository zodb/relstorage appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Shane Hathaway with Zope Foundation and Contributors" 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 RelStorage
Create a simple inventory management system using Python's RelStorage package as the backend storage mechanism. This system will allow users to add, update, delete, and view items stored in a relational database. Each item in the inventory should have properties such as name, description, quantity, and price. The goal is to demonstrate how RelStorage can efficiently manage persistent objects within a relational database context while providing a user-friendly interface for interacting with the data. Steps: 1. Set up a virtual environment and install necessary packages including RelStorage and ZODB. 2. Define a class structure for the inventory items, ensuring it is compatible with RelStorage's requirements. 3. Initialize RelStorage with your preferred relational database (e.g., PostgreSQL). 4. Create functions to add new items, update existing ones, delete items, and retrieve all items from the database. 5. Implement a simple command-line interface or a web-based UI for users to interact with the inventory system. 6. Test the system thoroughly to ensure all CRUD operations work correctly and efficiently. 7. Document the setup process, code structure, and usage instructions for future reference. Suggested Features: - Search functionality allowing users to find specific items by name or description. - Sorting options to display items in different orders based on quantity or price. - Logging of actions performed on the inventory for auditing purposes. - Integration with a front-end framework like Flask or Django for a more interactive experience.