ai-parrot-server

v0.25.1 suspicious
6.0
Medium Risk

Server infrastructure (handlers, MCP/A2A transports, scheduler, autonomous) for AI-Parrot

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several red flags including high shell and credential risks, moderate obfuscation, and network interactions, suggesting potential security issues. However, without direct evidence of malicious intent, it cannot be conclusively classified as malicious.

  • High shell risk indicating potential unexpected behavior
  • High credential risk due to insecure handling practices
  • Moderate obfuscation and network interactions
Per-check LLM notes
  • Network: The detected network patterns are typical for packages that interact with external services.
  • Shell: The use of shell commands to manage system services might indicate unexpected behavior unless documented functionality.
  • Obfuscation: The use of base64 decoding suggests potential obfuscation but could also be a standard practice for handling encoded data.
  • Credentials: Accessing environment variables for credentials can be legitimate, but the lack of secure handling practices raises suspicion of potential credential harvesting.
  • Metadata: The package is suspicious due to its newness and lack of maintainer details, but there's no concrete evidence of malice.

📦 Package Quality Overall: Medium (7.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2526 chars)
✦ High Contributing Guide 9.0

Has contribution guidelines and governance files

  • Governance file: security.py
  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 382 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in phenobarbital/ai-parrot
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • sl_ctx) session = aiohttp.ClientSession( timeout=aiohttp.ClientTimeout(total=self._t
  • try: async with aiohttp.ClientSession() as session: async with session.post(
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • decoded = base64.b64decode(auth_header[6:]).decode() username,
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • rvice_path, dest) subprocess.run( ["systemctl", "daemon-reload"],
  • ue, ) subprocess.run( ["systemctl", "enable", service_path.stem],
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • s.environ.get("S3_BUCKET") or os.environ.get("AWS_S3_BUCKET") if bucket: try:
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: phenobarbital.info>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository phenobarbital/ai-parrot appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 ai-parrot-server 
Create a fully functional mini-application named 'AI Parrot Hub' using the 'ai-parrot-server' Python package. This application will serve as a versatile communication and task management tool, leveraging the server infrastructure provided by 'ai-parrot-server'. Here’s a step-by-step guide on what your application should accomplish and how you can utilize the package's core features:

1. **Setup and Initialization**
   - Initialize the application with a clean and modular structure.
   - Integrate 'ai-parrot-server' into your project and configure it according to the documentation.

2. **User Authentication System**
   - Implement a simple user authentication system where users can sign up, log in, and manage their profiles.
   - Use session management to keep users logged in across different pages.

3. **Communication Hub**
   - Develop a feature that allows authenticated users to send messages to each other in real-time.
   - Utilize the handlers and transport mechanisms provided by 'ai-parrot-server' to facilitate seamless communication.

4. **Task Management**
   - Create a task management system where users can create, assign, and track tasks.
   - Implement scheduling capabilities using the scheduler component of 'ai-parrot-server' to remind users about upcoming deadlines.

5. **Autonomous Operations**
   - Design a feature that allows the system to perform certain operations autonomously based on predefined rules or triggers.
   - For instance, the system could automatically assign low-priority tasks to available users during off-peak hours.

6. **Monitoring and Analytics**
   - Include a dashboard that provides analytics and monitoring of the system's performance and user activity.
   - Use the data collected through the system to generate reports and insights.

7. **Security Measures**
   - Ensure all data transmitted between clients and the server is encrypted.
   - Implement security best practices as outlined in the 'ai-parrot-server' documentation.

8. **User Interface**
   - Develop a user-friendly interface using modern web technologies such as React or Vue.js.
   - Ensure the UI is responsive and accessible on various devices.

9. **Testing and Deployment**
   - Write comprehensive tests for your application to ensure reliability and functionality.
   - Deploy the application on a cloud platform like AWS or Heroku, ensuring it scales appropriately under load.

By following these steps and utilizing the 'ai-parrot-server' package effectively, you will create a robust and efficient mini-application that enhances communication and productivity among its users.