aworld

v0.3.2 suspicious
5.0
Medium Risk

Ant Agent Package

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its execution of shell commands and network activities, though there is no clear evidence of malicious intent. Further investigation into the necessity of these actions is recommended.

  • Executing shell commands
  • Performing network calls
Per-check LLM notes
  • Network: The package performs network calls to retrieve images and videos which is potentially legitimate but should be reviewed for the necessity of these actions.
  • Shell: Executing shell commands, especially involving package installation and build processes, could indicate risky behavior unless clearly justified by the package's purpose.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author 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: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 449 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in inclusionAI/AWorld
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • rt urllib.request urllib.request.urlretrieve(image_url, local_path) # --------------
  • rt urllib.request urllib.request.urlretrieve(video_url, local_path) async def _invoke_vi
  • pe: ignore resp = requests.get(image_url, stream=True, timeout=300) resp.raise_
  • pe: ignore resp = requests.get(video_url, stream=True, timeout=300) resp.raise_
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • ile): p = subprocess.Popen( ["pip", "install", "-U", "-r", requ
  • }") try: subprocess.check_call( ["sh", "-c", "npm install && npm run build"
  • port subprocess output = subprocess.check_output(cmd) return output.decode("utf-8") def get_build_date(
  • ist of args try: subprocess.check_call(cmd, shell=shell, timeout=60) except subprocess.CalledPr
  • isinstance(cmd, str): shell = True else: shell = False # cmd should be list of ar
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository inclusionAI/AWorld appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Ant AI" 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 aworld 
Your task is to create a fully-functional mini-application using the 'aworld' Python package, which simulates an ant colony for educational and simulation purposes. This application will serve as an interactive tool to demonstrate the complex behaviors and interactions within an ant colony. Here’s a detailed plan for your project:

1. **Project Setup**: Start by setting up your Python environment and installing the 'aworld' package. Ensure you have the necessary dependencies installed.

2. **Application Design**: Design your application to simulate different aspects of an ant colony, such as food gathering, nest building, and communication among ants. Use the 'aworld' package to handle the underlying mechanics of the ant behavior.

3. **Core Features**:
   - **Ant Colony Simulation**: Implement a basic simulation where ants move around a predefined environment, searching for food sources and returning to the nest. Utilize the 'aworld' package to define and control ant behaviors.
   - **Interactive Interface**: Develop a simple graphical user interface (GUI) using a library like Tkinter or Pygame. This interface should allow users to interact with the simulation, such as placing food sources or observing ant movements.
   - **Behavioral Analysis**: Include features that analyze and display key metrics about the ant colony's performance, such as the efficiency of food gathering, the health of the colony, and any emergent behaviors.

4. **Enhancements**:
   - **User Customization**: Allow users to customize various parameters of the simulation, such as the number of ants, the size of the environment, and the distribution of resources.
   - **Educational Content**: Integrate informational panels or tooltips that provide context about the simulation, explaining real-world ant behaviors and the scientific principles behind them.
   - **Data Visualization**: Implement graphs or charts to visualize data collected during the simulation, helping users understand patterns and trends in the colony's activities.

5. **Testing and Documentation**: Thoroughly test your application to ensure all features work as expected. Document your code and include a user guide that explains how to run and interact with the application.

6. **Deployment**: Prepare your application for deployment, ensuring it runs smoothly on different systems. Consider packaging it as an executable file or deploying it online.

Remember, the goal is to create an engaging and educational tool that leverages the capabilities of the 'aworld' package to simulate realistic ant colony behaviors.

💬 Discussion Feed

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