AI Analysis
Final verdict: SUSPICIOUS
The package shows no immediate signs of malicious intent but has some concerning metadata indicators. The incomplete maintainer information and lack of other packages from the same author warrant further investigation.
- Incomplete maintainer information
- Single package from maintainer
Per-check LLM notes
- Network: The detected network call pattern is typical for a library that interacts with an API over HTTP/HTTPS.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer's author information is incomplete and the maintainer has only one package, which may indicate a less established or potentially suspicious account.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 4 test file(s) found
Test runner config found: pyproject.toml4 test file(s) detected (e.g. test_auth.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (9768 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
147 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
score 1.5
Found 1 network call pattern(s)
xt()) self._session = aiohttp.ClientSession( base_url=self.base_url, timeout=tim
Code Obfuscation
No obfuscation patterns detected
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 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 aiounifiaccess
Create a mini-application called 'UniFi Access Monitor' that leverages the 'aiounifiaccess' Python package to monitor and manage devices connected to a UniFi network. This application will allow users to authenticate with their UniFi controller, retrieve a list of all connected devices, and perform basic management tasks such as changing device statuses (e.g., enabling/disabling) and viewing device details like MAC address, IP address, and connection status. Step 1: Set up the environment - Install Python and necessary libraries including 'aiounifiaccess'. - Configure your UniFi controller credentials and API endpoint. Step 2: Authenticate and Retrieve Data - Use 'aiounifiaccess' to connect to the UniFi controller and authenticate using provided credentials. - Fetch and display a list of all connected devices, including their MAC addresses, IP addresses, and current connection status. Step 3: Implement Device Management Features - Add functionality to enable/disable specific devices by their MAC address. - Include an option to view detailed information about any selected device. Step 4: Enhance User Experience - Design a simple command-line interface for easy interaction. - Implement error handling for common issues like invalid credentials or network timeouts. Suggested Features: - Real-time updates on device status changes. - Ability to filter devices by type (e.g., only show access points). - Support for exporting device lists to CSV files. This project will showcase the capabilities of 'aiounifiaccess' for interacting with UniFi networks and managing connected devices efficiently.
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