aioli-sdk

v1.12.0 suspicious
7.0
High Risk

Aioli (AI OnLine Inference), a platform for deploying AI models at scale.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks due to its ability to execute shell commands and potentially obfuscate code, raising concerns about its legitimacy and intent.

  • High shell risk indicating potential for arbitrary command execution
  • Moderate obfuscation risk suggesting possible hidden malicious functionality
Per-check LLM notes
  • Network: The network call pattern suggests potential for external communication which could be used for legitimate purposes but also raises concerns about possible unauthorized data exchange.
  • Shell: The shell execution patterns indicate direct system command execution, which poses a significant risk for potential misuse including executing arbitrary commands or installing software without user consent.
  • Obfuscation: The use of base64 decoding and YAML loading suggests potential obfuscation to hide code logic or configurations.
  • Credentials: No direct evidence of credential harvesting is found, but caution should still be exercised.
  • Metadata: The maintainer has a new or inactive PyPI account and the package lacks detailed metadata, indicating potential low effort or oversight.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

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

Some documentation present

  • Brief PyPI description (368 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

  • 399 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • ts for event in self.socket.connect(ping_rate=0): if isinstance(event, events.Connec
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ump(yaml.safe_load( # base64.b64decode(source)), default_flow_style=False) # if not isinstance
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ed ANSI escape codes. os.system("") # we lazily import "det deploy" but in the future w
  • ).strip() assert 0 == os.system("aioli --version") actual = os.popen("aioli --versio
  • .0.1-bad" assert 0 == os.system("aioli --version") actual = os.popen("aioli --versio
  • no entries yet assert os.system("aioli registry list") == 0 expected = (
  • assert ( os.system( "aioli registry create --type s3 --access-k
  • assert ( os.system("aioli registry update --no-insecure-https bento-registry-in
✓ 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 score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "HPE AI Solutions" 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 aioli-sdk 
Create a real-time image classification web application using the Aioli SDK in Python. This application will allow users to upload images, and then use pre-trained AI models hosted on the Aioli platform to classify the content of the images. Here’s a detailed plan for the project:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing necessary packages including `aioli-sdk` and any other dependencies such as Flask for the web framework.
2. **Project Structure**: Organize your project into modules for better management. Have separate files for configuration, API calls to Aioli, image processing, and the web interface.
3. **API Integration**: Use the Aioli SDK to interact with the Aioli platform. This includes authenticating requests, listing available models, and making inference requests.
4. **Web Interface**: Develop a simple but user-friendly web interface using Flask where users can upload images. Ensure the design is responsive and accessible.
5. **Image Classification Functionality**: Implement functionality to process uploaded images, send them to the Aioli platform for classification, and display the results back to the user. Include options to choose different models if available from the Aioli platform.
6. **Error Handling & Logging**: Ensure robust error handling and logging mechanisms are in place to capture and report errors gracefully.
7. **Security Considerations**: Pay attention to security best practices, such as validating uploaded files, sanitizing inputs, and securing sensitive data.
8. **Testing**: Write unit tests for critical parts of your application, especially around the integration with the Aioli platform and the image processing logic.
9. **Documentation**: Provide clear documentation on how to set up and run the application, including setup instructions for the Aioli SDK and any other required configurations.
10. **Deployment**: Plan for deployment, considering cloud platforms like AWS or Heroku, ensuring the application is scalable and performant.

This project aims to showcase the power of the Aioli SDK in enabling real-world applications that leverage AI without needing deep expertise in model deployment.

💬 Discussion Feed

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