ailia-llm

v1.4.1.0 suspicious
4.0
Medium Risk

ailia LLM

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a low risk profile based on current analysis, but the lack of open-source licensing and limited maintainer information raises concerns about its legitimacy and future security.

  • Not open source
  • Limited maintainer information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for functionality.
  • Shell: No shell execution detected, indicating no direct system command execution risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer seems new and the package lacks detailed metadata, indicating low effort or a possibly inactive project.

📦 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 (1451 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

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

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

Email domain looks legitimate: ailia.ai

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "ailia Inc." 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 ailia-llm 
Create a personalized news summarizer app using the ailia-llm package in Python. This app will allow users to input a URL of a news article and receive a concise summary of its content. Additionally, the app should include sentiment analysis to gauge the tone of the article and suggest related articles based on the user's interests.

Step 1: Setup the Project Environment
- Install Python and necessary libraries including ailia-llm.
- Set up a virtual environment for your project.

Step 2: Design the User Interface
- Use a simple web framework like Flask or Django to create a basic UI where users can input URLs.
- Ensure the UI is responsive and user-friendly.

Step 3: Implement News Article Fetching
- Develop a function to fetch the content from the provided URL.
- Handle common issues such as broken links or non-accessible pages gracefully.

Step 4: Summarize the News Article
- Utilize the ailia-llm package to process the fetched content and generate a summary.
- Ensure the summary captures the essence of the article while being concise.

Step 5: Perform Sentiment Analysis
- Apply sentiment analysis techniques using ailia-llm to determine if the article's tone is positive, negative, or neutral.
- Display the sentiment score alongside the summary.

Step 6: Recommend Related Articles
- Based on the user's browsing history and preferences, recommend similar articles.
- Use ailia-llm to analyze the content of the recommended articles and ensure they match the user's interests.

Step 7: Test and Deploy the Application
- Thoroughly test the app with various types of news articles.
- Deploy the application to a cloud service provider for public access.

Suggested Features:
- Allow users to save summaries for later reference.
- Include a feature to share summaries via social media platforms.
- Offer customization options for the summary length and sentiment analysis depth.

How ailia-llm is Utilized:
- For text summarization, ailia-llm provides advanced models that can understand context and generate coherent summaries.
- For sentiment analysis, ailia-llm offers tools that can accurately assess the emotional tone of the text.
- By leveraging these capabilities, the app can provide valuable insights and summaries that enhance the user's reading experience.

💬 Discussion Feed

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