aks-shannon-entropy

v0.1.0 suspicious
4.0
Medium Risk

Compute shannon entropy from LLM responses to detect hallucinations

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no signs of direct malicious activity but raises flags due to missing repository and a potentially inactive maintainer.

  • Metadata risk score is high due to missing repository and possibly inactive maintainer.
  • No evidence of obfuscation or credential harvesting.
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The repository not being found and the maintainer having a new or inactive account raises concerns.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Detailed PyPI description (873 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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 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

  • Only one version has ever been released — brand new package
  • Author "Semantic Uncertainty Authors" 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 aks-shannon-entropy 
Develop a Python-based mini-application that leverages the 'aks-shannon-entropy' package to evaluate the reliability of responses generated by large language models (LLMs). This tool aims to identify potential hallucinations within LLM outputs by calculating Shannon entropy, which measures the unpredictability or randomness of information. Here’s a detailed guide on how to proceed:

1. **Project Setup**: Begin by setting up your development environment with Python and installing necessary packages including 'aks-shannon-entropy'. Ensure you also have a basic understanding of how LLMs work.
2. **API Integration**: Integrate your application with an API endpoint of an LLM such as OpenAI's API. Your app should be able to send prompts to the LLM and receive responses.
3. **Entropy Calculation**: Utilize the 'aks-shannon-entropy' package to compute Shannon entropy for each response received from the LLM. Higher entropy values may indicate more unpredictable or less reliable content.
4. **Threshold Setting**: Define a threshold value for entropy. Responses with entropy above this threshold could be flagged as potentially unreliable or containing hallucinations.
5. **User Interface**: Develop a simple user interface where users can input their own prompts to test against the LLM. Display both the original LLM response and its calculated entropy score.
6. **Advanced Features** (Optional): Consider adding features like historical data tracking, visual representations of entropy over time, or comparison between different LLMs based on their entropy scores.
7. **Documentation & Testing**: Provide comprehensive documentation explaining the setup process, usage instructions, and any limitations. Conduct thorough testing to ensure accuracy and reliability of entropy calculations.

This project not only enhances your skills in working with AI and machine learning but also contributes valuable insights into evaluating the reliability of AI-generated content.

💬 Discussion Feed

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