AI Analysis
Final verdict: SUSPICIOUS
The package shows some concerning signs such as potential shell execution risks and obfuscated code, despite showing no clear evidence of malicious intent or network/credential risks. The low maintenance effort and potential unverified authorship add to the suspicion.
- Potential shell execution risks
- Obfuscated code
Per-check LLM notes
- Network: No network calls detected, which is normal and does not indicate any risk.
- Shell: The use of shell execution might be legitimate depending on the package's functionality, but it could also pose a risk if not properly sanitized or controlled.
- Obfuscation: The code snippet shows obfuscation through string manipulation which may indicate an attempt to hide logic or source code, but it's not definitively malicious without further context.
- Credentials: No clear patterns of credential harvesting are present in the provided code snippet.
- Metadata: The package shows low maintenance effort and could be from an unverified author, but there's no direct evidence of malicious intent.
Package Quality Overall: Medium (5.8/10)
✦ High
Test Suite
9.0
Test suite present — 20 test file(s) found
Test runner config found: conftest.py20 test file(s) detected (e.g. conftest.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (15399 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
302 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 100 commits in gmarzot/aiomoqtSmall but multi-author team (3–4 contributors)
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
return 2 set_log_level(__import__('logging').WARNING) state = BenchState() stats = LiveStats(ob
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
g.open("w") as f: subprocess.run(cmd, stdout=f, stderr=subprocess.STDOUT,
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: marzresearch.net>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository gmarzot/aiomoqt appears legitimate
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 aiomoqt
Create a real-time IoT monitoring system using Python's 'aiomoqt' library. This system will connect to multiple MQTT brokers to gather sensor data from various IoT devices, process this data in real-time, and visualize it on a web dashboard. The application should allow users to subscribe to specific topics to receive updates on particular sensors or devices. ### Features: 1. **Real-Time Data Collection:** Use 'aiomoqt' to establish connections with multiple MQTT brokers. The application should support subscribing to multiple topics simultaneously to collect data from different sensors. 2. **Data Processing:** Implement real-time processing of incoming data. For example, calculate average temperatures over a period, detect anomalies based on historical data, etc. 3. **Web Dashboard:** Develop a simple web interface using Flask or a similar framework to display the collected data in real-time. The dashboard should allow users to select which topics they want to monitor. 4. **User Authentication:** Add basic user authentication to the web dashboard to ensure only authorized users can access the data. 5. **Alert System:** Set up an alert system that notifies users via email or SMS when certain conditions are met (e.g., temperature exceeds a threshold). 6. **Historical Data Storage:** Store collected data in a database (like SQLite or PostgreSQL) for future analysis. 7. **API Interface:** Expose an API endpoint that allows other applications to query historical data or current status. ### How to Utilize 'aiomoqt': - Use 'aiomoqt' to handle MQTT connections asynchronously, allowing your application to efficiently manage multiple subscriptions without blocking the main thread. - Leverage 'aiomoqt' to publish any processed data back to MQTT brokers if necessary, enabling integration with other IoT systems. - Employ 'aiomoqt' for its robust error handling and reconnection capabilities to ensure stable operation even under network instability.
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