aio-lib-sandbox

v0.1.0a7 safe
4.0
Medium Risk

Python SDK for Adobe Runtime Sandboxes

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories, with no clear signs of malicious activity. While there is some potential for obfuscation, it is not indicative of malicious intent.

  • Low risk scores across network, shell, credential, and metadata checks.
  • Base64 decoding present, but likely for legitimate purposes.
Per-check LLM notes
  • Network: The use of HTTP requests is typical for network communication and may be part of the package's intended functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: Base64 decoding is commonly used for obfuscation but could also be part of legitimate functionality, such as handling encrypted or compressed data.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The package shows low effort in its metadata and maintainer history, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Detailed PyPI description (6176 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

  • 53 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 score 3.0

Found 2 network call pattern(s)

  • n.dumps(body) async with httpx.AsyncClient(verify=verify_ssl, timeout=timeout) as client: try:
  • try: async with httpx.AsyncClient(verify=self.verify_ssl) as client: try:
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • 4": content = base64.b64decode(content).decode() self.resolve_file_op(exec_id,
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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

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 aio-lib-sandbox 
Create a Python-based mini-application that integrates with Adobe Runtime Sandboxes using the 'aio-lib-sandbox' package. Your goal is to develop a utility that allows users to manage their Adobe sandbox environments more efficiently. This tool will enable the creation, modification, and deletion of sandbox instances, as well as the deployment of custom scripts within these sandboxes.

### Key Features:
- **Sandbox Management**: Users should be able to create new sandbox instances, modify existing ones, and delete unused ones.
- **Script Deployment**: Implement functionality to upload and execute custom scripts within sandbox environments.
- **Status Monitoring**: Provide real-time status updates on sandbox operations and script execution.
- **User Interface**: Develop a simple command-line interface (CLI) for user interaction.
- **Error Handling**: Ensure robust error handling mechanisms are in place to handle various scenarios such as network issues or invalid inputs.

### Steps to Build the Application:
1. **Setup Environment**:
   - Install Python and necessary libraries including 'aio-lib-sandbox'.
   - Configure your development environment (IDE, virtual environment).
2. **API Integration**:
   - Authenticate with Adobe's API to gain access to sandbox management functions.
   - Use 'aio-lib-sandbox' to interact with the Adobe Runtime Sandboxes API.
3. **Core Functionality Development**:
   - Write functions to create, update, and delete sandbox instances.
   - Implement a method to deploy and run custom scripts within the sandboxes.
4. **CLI Development**:
   - Create a CLI interface using Python’s built-in capabilities or a library like Click.
   - Design commands for each feature allowing users to easily manage their sandboxes from the terminal.
5. **Testing and Debugging**:
   - Thoroughly test all functionalities.
   - Fix any bugs and refine user experience based on feedback.
6. **Documentation**:
   - Document all steps involved in setting up the application and using its features.
   - Include examples and best practices.

### Utilizing 'aio-lib-sandbox':
- Use the 'aio-lib-sandbox' package to facilitate asynchronous communication with Adobe's API endpoints.
- Leverage its features to streamline sandbox management tasks and enhance the performance of your application.

By following these guidelines, you'll develop a powerful yet user-friendly tool that simplifies working with Adobe Runtime Sandboxes.