automonisaur

v0.7.6 suspicious
4.0
Medium Risk

Core libraries for automonisaur

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate suspicion due to potential shell execution risks and the maintainer's limited package history, though no direct malicious activities were detected.

  • Shell risk detected due to possible un-sanitized shell execution.
  • Maintainer has only one package, raising questions about their credibility.
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Detection of shell execution attempts without proper sanitization could indicate potential risk for executing arbitrary commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

  • 16 test file(s) detected (e.g. test_client.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5137 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

  • 92 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in TheShellLand/automonisaur
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: self.call = subprocess.Popen( args=command, stdout=subpro
  • are not supported! To use run(shell=True). {command=}' logger.error(f'Run :: sanitize_comman
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository TheShellLand/automonisaur appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "naisanza" 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 automonisaur 
Create a Python-based mini-application named 'AutoMoniTool' that leverages the 'automonisaur' package to automate system monitoring tasks on Linux systems. This tool should be designed to run continuously in the background, periodically collecting and analyzing system metrics such as CPU usage, memory usage, disk space, network traffic, and process health. The application should then log these metrics to a local file and optionally send alerts via email or SMS if any of the monitored parameters exceed predefined thresholds.

Step-by-Step Instructions:
1. Set up a virtual environment for the project and install the 'automonisaur' package along with other necessary dependencies like psutil for system monitoring.
2. Develop a configuration file where users can specify thresholds for CPU usage, memory usage, disk space, and network traffic. Also, allow users to configure alert methods (email/SMS).
3. Implement a function using 'automonisaur' to collect system metrics at regular intervals (e.g., every 5 minutes). Use the package's capabilities to efficiently gather data from the OS.
4. Create a logging mechanism to store collected metrics in a human-readable format (CSV or JSON) to a specified directory.
5. Integrate functionality to compare collected metrics against user-defined thresholds and trigger alerts when conditions are met. Utilize external services or libraries to handle sending alerts based on the configured method.
6. Ensure the application can be started manually or set up to run as a daemon/service on Linux systems for continuous operation.
7. Add a command-line interface (CLI) for users to interact with the application, allowing them to start/stop the monitoring service, view logs, and adjust configurations without needing to modify files directly.
8. Finally, document the setup process, configuration options, and CLI commands clearly so that users can easily deploy and manage 'AutoMoniTool'.

Utilization of 'automonisaur':
- Use 'automonisaur' to streamline the collection of system metrics, ensuring efficient and reliable data gathering.
- Leverage 'automonisaur's advanced features for processing and analyzing collected data to provide meaningful insights into system performance.
- Incorporate 'automonisaur's support for integrating with various external services to enhance the alerting functionality.

💬 Discussion Feed

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