aions-llm

v1.0.8 suspicious
5.0
Medium Risk

Actions and Interface Object Notation: An open standard for LLM tools.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to the high obfuscation risk from the use of 'eval', despite having low risks in network, shell, credential, and metadata areas.

  • High obfuscation risk due to 'eval' usage
  • No immediate supply-chain attack indicators but further scrutiny is recommended
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or unauthorized system access.
  • Obfuscation: Use of 'eval' with dynamic strings can indicate an attempt to bypass static analysis and may be used for malicious purposes.
  • Credentials: No direct evidence of credential harvesting patterns found.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, but there are no other red flags.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Detailed PyPI description (7301 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

  • 6 type-annotated function signatures (partial)
○ 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

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • tool_data['args_schema'] = eval(schema_str, context) except Exception as
  • func_data["executable"] = eval(func_str, context) except Exception as e
✓ 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 2.0

1 maintainer concern(s) found

  • Author "Sourav Modak" 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 aions-llm 
Create a Python-based mini-application that integrates with various Language Learning Machines (LLM) tools using the 'aions-llm' package. Your goal is to develop an educational tool that helps users learn new languages through interactive dialogues and quizzes. The application should allow users to select a language they want to learn and interact with the LLM to practice speaking and writing in that language. Here’s a detailed breakdown of the project steps and features:

1. **Setup and Configuration**: Begin by installing the 'aions-llm' package and setting up your development environment. Understand how to configure the package to connect with different LLMs.
2. **User Interface Design**: Develop a simple yet intuitive user interface where users can choose from a list of supported languages. This could be a CLI interface or a basic web app depending on your preference.
3. **Language Selection**: Implement functionality that allows users to select a language they wish to learn. Once selected, the application should initialize the appropriate LLM model through 'aions-llm'.
4. **Interactive Dialogues**: Use 'aions-llm' to facilitate natural language conversations between the user and the LLM. The LLM should act as a language tutor, engaging in dialogues that simulate real-life conversation scenarios.
5. **Quizzes and Assessments**: Incorporate quiz features where the LLM generates questions based on the user's language level and provides feedback on their responses. These quizzes should cover grammar, vocabulary, and comprehension.
6. **Progress Tracking**: Keep track of the user's progress over time. Store data such as completed lessons, quiz scores, and areas needing improvement.
7. **Customization Options**: Allow users to customize their learning experience. For example, they might want to focus more on speaking practice or prefer reading exercises.
8. **Integration Testing**: Test the integration of 'aions-llm' with multiple LLMs to ensure compatibility and robustness of your application.
9. **Documentation and Deployment**: Document your setup process, code structure, and how to use the application. Optionally, deploy your application online so others can try it out.

By completing this project, you will not only enhance your skills in integrating AI tools but also contribute to the development of accessible language learning resources.

💬 Discussion Feed

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