ai-parrot-tools

v0.1.60 suspicious
6.0
Medium Risk

Tools and toolkits for AI-Parrot agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several behaviors that are potentially risky, including direct access to AWS credentials, execution of shell commands, and base64 decoding. While these actions could be part of legitimate functionality, they raise significant concerns about potential misuse.

  • Direct access to AWS credentials
  • Execution of shell commands
  • Base64 decoding
Per-check LLM notes
  • Network: The use of HTTP clients suggests the package might interact with external services, but without context, it's hard to determine if this is expected behavior.
  • Shell: Executing shell commands and running tests via subprocess could indicate legitimate functionality, but also poses risks for arbitrary command execution which may be exploited.
  • Obfuscation: Base64 decoding is commonly used for data encoding but may indicate obfuscation if used to hide code or data.
  • Credentials: The code accessing AWS credentials directly raises suspicion as it may be harvesting secrets for unauthorized access.
  • Metadata: The author's details are incomplete and the account seems new or inactive, raising some concerns but not definitive signs of malice.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/phenobarbital/ai-parrot/
  • Detailed PyPI description (2649 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 403 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)

  • try: async with httpx.AsyncClient() as client: response = await client.get(
  • } async with aiohttp.ClientSession() as session: async with session.get(
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • log_result = base64.b64decode(log_result).decode("utf-8") return {
  • code_params["ZipFile"] = base64.b64decode(zip_file) elif s3_bucket and s3_key:
  • params["ZipFile"] = base64.b64decode(zip_file) elif s3_bucket and s3_key:
  • img_data = base64.b64decode(heatmap_b64) with open(file_path, 'wb')
  • g, sqrt, pi, e return eval(expression, {"x": val, "np": np, "__builtins__": {}},
  • def f(val): return eval(expression, {"x": val, "__builtins__": {}},
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • setup_commands: result = subprocess.run(cmd.split(), capture_output=True) if result.returncode !
  • xit(1) # Run pytest result = subprocess.run( ["pytest", "-v", "/workspace/test_main.py"], captur
  • nt) result = subprocess.run( ["python", str(code_file)],
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • tion="AWS profile name from ~/.aws/credentials" ) aws_region: str = Field( default=AWS_DE
✓ 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 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 ai-parrot-tools 
Create a fully functional mini-application called 'AI Parrot Companion' that leverages the 'ai-parrot-tools' package to enhance user interaction and data processing capabilities. This application will serve as a versatile tool for users to communicate with AI agents in a more engaging and efficient manner. Here are the steps and features you need to include:

1. **Setup**: Begin by installing the 'ai-parrot-tools' package using pip. Ensure your environment supports Python 3.8 or higher.
2. **Core Functionality**: Implement a chat interface where users can input text and receive responses from an AI agent powered by the 'ai-parrot-tools'. The AI should be able to understand natural language inputs and generate contextually relevant outputs.
3. **Enhanced Features**:
   - **Contextual Memory**: Utilize the 'ai-parrot-tools' to maintain a session history between the user and the AI, allowing the AI to remember past interactions and use them to inform future responses.
   - **Customization Options**: Allow users to customize their AI agent's personality traits such as tone, humor level, and formality through settings provided by 'ai-parrot-tools'.
   - **Data Analysis**: Incorporate tools from 'ai-parrot-tools' to analyze user interactions, providing insights into common queries and patterns.
4. **User Interface**: Develop a clean, intuitive UI using a framework like Tkinter or Streamlit for desktop applications or Flask/Django for web-based ones. Ensure the UI is responsive and accessible.
5. **Testing and Validation**: Use test cases and real user feedback to refine the AI's responses and improve the overall user experience. Validate that the 'ai-parrot-tools' functionalities are correctly integrated and enhance the application's performance.
6. **Documentation**: Provide comprehensive documentation on how to install and use the 'AI Parrot Companion', including details on how the 'ai-parrot-tools' package contributes to the application's functionality.

This project aims to demonstrate the versatility of the 'ai-parrot-tools' package in creating interactive and personalized AI experiences.