aivudaos

v1.0.0.dev2026051402 safe
3.0
Low Risk

AivudaOS packaged for pip installation and PyPI publishing.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk indicators, with no signs of obfuscation or credential harvesting. While there are some concerns regarding metadata quality and maintainer activity, these alone do not conclusively point towards malicious intent.

  • No obfuscation patterns detected
  • No credential harvesting patterns detected
  • Metadata quality and maintainer activity raise minor concerns
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising concerns but not conclusive evidence of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • 3 test file(s) detected (e.g. test_app_manifest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4325 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

  • 285 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

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • command.name completed = subprocess.run(["bash", script_path, *forwarded_args], env=env, check=False
  • ipt_name) completed = subprocess.run( ["bash", str(script_path)], check=F
  • ) completed = subprocess.run( [ "systemd-run",
  • return subprocess.Popen( ["bash", "-lc", command], stdin=sub
  • try: completed = subprocess.run( ["systemctl", "--user", *args],
  • try: completed = subprocess.run( ["systemd-run", "--user", "--version"],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:80
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aivudaos 
Create a simple system monitoring tool using the 'aivudaos' package. This tool will run on a Linux-based system and provide real-time monitoring of CPU usage, memory usage, network traffic, and disk I/O operations. It should also be able to log these metrics over time and generate a basic report at regular intervals.

Steps:
1. Install the 'aivudaos' package via pip.
2. Import necessary modules from 'aivudaos' for interfacing with the system hardware.
3. Define functions to periodically gather data about CPU usage, memory usage, network traffic, and disk I/O.
4. Implement logging functionality to store collected data into a file.
5. Create a reporting mechanism that summarizes logged data every hour and outputs it to the console.
6. Ensure the application runs as a background service, continuously updating metrics and generating reports.

Suggested Features:
- Graphical display of real-time metrics using matplotlib or similar visualization library.
- Command-line interface for starting/stopping the monitoring service and adjusting reporting intervals.
- Ability to configure which metrics are monitored through a configuration file.
- Email alerting when certain thresholds are exceeded.

How 'aivudaos' is Utilized:
- Use 'aivudaos' to access low-level system information and control capabilities not directly available through standard Python libraries. For example, accessing raw hardware counters for more accurate performance measurements.

💬 Discussion Feed

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