backend.ai-kernel

v26.4.3 suspicious
4.0
Medium Risk

Backend.AI Kernel Runner

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to direct shell execution capabilities, which can be exploited for command injection. However, the lack of credential harvesting and minimal network interaction reduce the overall threat level.

  • High shell risk due to direct execution
  • Low credential and network risk
Per-check LLM notes
  • Network: The network call appears to be a health check which is generally benign.
  • Shell: Direct shell execution can pose risks if not properly sanitized, suggesting potential for command injection and control.
  • Obfuscation: The code uses Base64 and msgpack for data decoding, which could be used for obfuscation but is also common in legitimate applications.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.backend.ai/
○ 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

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

Active multi-contributor project

  • 9 unique contributor(s) across 100 commits in lablup/backend.ai
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • None, urllib.request.urlopen, health_check_endpoint )
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ) -> list[Any]: bindata = base64.b64decode(data) result: list[Any] = msgpack.unpackb(bindata, raw=F
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • ( shell=True, iopub=True, stdin=True, hb=True )
✓ 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 lablup/backend.ai appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Lablup Inc. and contributors" 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 backend.ai-kernel 
Create a Python-based mini-application named 'AI Playground' that leverages the Backend.AI Kernel Runner package to provide users with an interactive environment for running various AI models on Backend.AI clusters. This application will allow users to select from a library of pre-configured AI models and execute them directly within the app, receiving real-time output and results.

The core functionalities of the 'AI Playground' include:
- A user-friendly interface for selecting different AI models (e.g., image classification, sentiment analysis)
- Integration with Backend.AI clusters for resource allocation and execution of models
- Real-time visualization of model outputs and performance metrics
- Support for multiple programming languages (Python, R, etc.)
- Ability to save and share model executions and results

Steps to develop the application:
1. Set up the development environment with necessary Python packages including 'backend.ai-kernel'.
2. Design the UI/UX layout for the application using a Python GUI toolkit like Tkinter or PyQt.
3. Implement functionality to connect to Backend.AI clusters using the 'backend.ai-kernel' package.
4. Develop a module to load and display a list of available AI models and their descriptions.
5. Create an execution engine that sends selected models to the Backend.AI cluster for processing and returns results.
6. Add features for visualizing outputs such as charts, graphs, or images based on the model type.
7. Integrate error handling and logging mechanisms for troubleshooting.
8. Implement saving and sharing options for model executions and their results.
9. Test the application thoroughly across different scenarios and environments.
10. Document the codebase and prepare installation instructions for end-users.

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