aiel-cli

v1.3.9 suspicious
5.0
Medium Risk

AI Execution Layer CLI

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks associated with network and credential handling, indicating potential vulnerabilities that could be exploited. However, there is no clear evidence of malicious intent.

  • network calls to GCS without proper authentication
  • potential credential harvesting through keyring and getpass
Per-check LLM notes
  • Network: Detected network calls to GCS without proper authentication token handling may indicate unauthorized access attempts or data exfiltration.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No signs of obfuscation patterns detected.
  • Credentials: The code indicates potential credential harvesting through keyring and getpass functions.
  • Metadata: The package shows low maintainer activity and poor metadata quality, raising some concerns but not strong evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • 4 test file(s) detected (e.g. test_support_flow.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (11809 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

  • 157 type-annotated function signatures detected in source
○ 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 score 7.5

Found 5 network call pattern(s)

  • GCS; no X-API-Token. r = httpx.put( signed_url, headers={"Content-Type": conten
  • Token": f"{token}"} with httpx.Client(timeout=10.0) as client: r = client.get(url, headers
  • if all else None # with httpx.Client(timeout=10.0) as client: # r = client.post(url, head
  • es or fails. """ with httpx.Client(timeout=30.0) as client: # fresh remote if p
  • w workspace/project. with httpx.Client(timeout=30.0) as client: integrations_manifest = _dp
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 7.5

Found 3 credential access pattern(s)

  • return None return keyring.get_password(KEYRING_SERVICE, profile) def _keyring_del(profile: str) -
  • None try: return keyring.get_password(KEYRING_SERVICE, profile) except Exception: retu
  • t hidden).")) token = getpass.getpass("AIEL_TOKEN: ").strip() if not token: raise typ
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aiel-cli 
Your task is to create a fully functional mini-application named 'AIRecipeGenerator' using the Python package 'aiel-cli'. This application will serve as a tool for generating unique and creative recipes based on user inputs. The goal is to provide users with an engaging and personalized culinary experience, leveraging the power of artificial intelligence to suggest innovative recipe ideas.

The application should have the following core functionalities:
1. **User Input**: Allow users to input ingredients they have at home or specific dietary preferences such as vegan, gluten-free, etc.
2. **Recipe Generation**: Utilize 'aiel-cli' to execute an AI model that generates a list of recipes based on the user's inputs. The AI model should be capable of suggesting recipes that are not only suitable for the given ingredients but also align with any specified dietary restrictions.
3. **Recipe Display**: Present the generated recipes in a readable format, including the list of ingredients required, cooking steps, and nutritional information if available.
4. **Interactive Feedback**: Provide an option for users to rate the generated recipes after trying them out. This feedback will be used to improve future recipe suggestions.
5. **User Profile Management**: Users should be able to save their favorite recipes and view their past ratings and saved recipes.

To achieve these functionalities, you will need to integrate 'aiel-cli' to handle the AI execution layer, which includes setting up the environment, deploying the AI model, and processing the user inputs. Additionally, consider utilizing other Python libraries such as Flask for the web interface, SQLAlchemy for database management, and BeautifulSoup for scraping additional nutritional data from external sources if necessary.

This project aims to demonstrate the practical application of AI in everyday life, making it easier for people to discover new recipes and enjoy cooking.

💬 Discussion Feed

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