augllm

v2.4.2 suspicious
6.0
Medium Risk

This is LLM interface library.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to potential shell command execution and obfuscated code, although no clear signs of malicious intent were identified. Low maintainer activity and poor metadata quality add to the suspicion.

  • Shell risk due to potential control over external services
  • Possible obfuscation in code
Per-check LLM notes
  • Network: The network call appears to be checking the health status of a server, which is generally benign.
  • Shell: Executing commands like 'ollama stop' could potentially control external services, suggesting higher risk for unauthorized operations.
  • Obfuscation: The pattern suggests possible obfuscation but could also be legitimate use of base64 encoding for image handling.
  • Credentials: No clear patterns indicating credential harvesting were found.
  • Metadata: The package shows low maintainer activity and poor metadata quality, raising some suspicion but not conclusive evidence of malice.

📦 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 (1348 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

  • 41 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 1.5

Found 1 network call pattern(s)

  • y: response = requests.get(health_url) if response.status_code == 200:
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ")[1] data = base64.b64decode(encoded) img = Image.open(
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • self.server_process = subprocess.Popen( self.cmd_base, stdout=subprocess.D
  • try: result = subprocess.run( ["ollama", "stop", self.model],
✓ 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 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 augllm 
Develop a personalized recommendation system using the 'augllm' package in Python. Your task is to create a user-friendly web application that takes user preferences as input and suggests relevant items such as books, movies, or music based on those preferences. The application will leverage the capabilities of 'augllm' to enhance the recommendation engine's performance and personalization level.

Steps to complete this project:
1. Set up your development environment with Python, Flask (or any preferred web framework), and install the 'augllm' package.
2. Design a simple UI where users can input their preferences (e.g., genres they like, specific titles they enjoyed).
3. Implement a backend that processes these inputs using 'augllm'. Use 'augllm' to fine-tune a pre-trained language model on a dataset related to the items you're recommending (e.g., movie reviews, book summaries).
4. Develop an algorithm within your application that uses the processed outputs from 'augllm' to generate recommendations tailored to each user.
5. Integrate a feature to allow users to rate the recommended items, which can then be used to further refine the recommendation model over time.
6. Test your application thoroughly to ensure it handles various types of input data gracefully and provides accurate recommendations.
7. Deploy your application on a platform like Heroku or AWS so others can use it too!

Suggested Features:
- A clean, responsive design suitable for both desktop and mobile devices.
- An option for users to sign up/log in, allowing them to save their preferences and view their history of ratings/recommendations.
- Real-time feedback on how the recommendation system adapts to user interactions.
- Integration with external APIs (such as IMDB or Goodreads) to fetch more details about recommended items.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!