apppack-stats

v0.6.0 suspicious
5.0
Medium Risk

Live response-time stats from streamed AppPack web logs.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits shell execution, which increases its risk profile. While there are no direct signs of malicious activities like obfuscation or credential harvesting, the recent and rapid activity in the repository, combined with the maintainer's lack of history, raises concerns about potential supply-chain risks.

  • Shell execution detected, increasing risk of misuse or unintended behavior.
  • Repository shows recent and rapid activity with minimal maintainer history.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: Shell execution patterns suggest potential execution of external commands, which could be legitimate but also indicates a higher risk for potential misuse or unintended behavior.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
  • Metadata: The repository's recent and rapid activity, along with the maintainer's lack of history and details, raises suspicion.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • Test runner config found: pyproject.toml
  • 4 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3649 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 28 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 15 commits in bartTC/apppack-stats
  • Single author with few commits — possibly a personal or throwaway project

🔬 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: proc = subprocess.run( # noqa: S603 cmd, input=text, text=True, c
  • ix", args.prefix] proc = subprocess.Popen( # noqa: S603 cmd, stdout=subprocess.PIPE,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: elephant.house>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • All 15 commits happened within 24 hours
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 apppack-stats 
Create a real-time monitoring tool using Python that leverages the 'apppack-stats' package to analyze live response times from streamed web logs of a hypothetical web application named 'AppPack'. This tool will serve as a dashboard for developers and system administrators to monitor the performance of their web applications in real-time. The application should have the following features:

1. **Real-Time Log Streaming**: Implement functionality to stream log data from a simulated or actual AppPack web server in real-time.
2. **Response Time Analysis**: Use 'apppack-stats' to calculate and display the average, minimum, and maximum response times over a specified time window.
3. **Visualization**: Integrate a simple graphical interface (using libraries such as Matplotlib or Plotly) to visually represent the response time trends over time.
4. **Alert System**: Develop an alert mechanism that notifies users via email or console logs when response times exceed a predefined threshold.
5. **Customizable Thresholds**: Allow users to set custom thresholds for response times based on their specific requirements.
6. **Log Filtering**: Provide options to filter logs based on HTTP status codes, request types (GET, POST), and other relevant criteria to focus on specific aspects of performance.
7. **Data Export**: Enable exporting of analyzed data into CSV format for further analysis or reporting purposes.

Your task is to design and implement this application from scratch, ensuring it is modular, well-documented, and user-friendly. Make sure to utilize 'apppack-stats' effectively to handle the statistical calculations and consider integrating additional Python packages as necessary for other functionalities like GUI, logging, and email notifications.

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