ai-track

v0.6.1 suspicious
4.0
Medium Risk

Universal AI runtime for local and remote inference.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to potential shell execution and incomplete metadata, suggesting possible security concerns. However, there is no concrete evidence of malicious activities.

  • Potential shell execution
  • Incomplete maintainer's author information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: Potential risk due to shell execution that could be used to execute arbitrary commands, depending on how it's implemented and controlled.
  • Obfuscation: The use of base64 decoding with validation suggests potential obfuscation or encryption but does not definitively indicate malicious intent.
  • Credentials: No clear patterns of credential harvesting were detected.
  • Metadata: The maintainer's author information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 18 test file(s) found

  • 18 test file(s) detected (e.g. test_additional_coverage.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8228 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

  • 463 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 59 commits in langelabs/ai-track
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • ry: image_bytes = base64.b64decode(encoded_bytes, validate=True) except ValueError as e
  • try: audio_bytes = base64.b64decode(audio_b64, validate=True) except ValueError as error:
  • return self def eval(self) -> None: """Simulate eval mode setup."""
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • I dependency.""" result = subprocess.run( [ sys.executable, "-c",
✓ 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

Repository langelabs/ai-track appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 ai-track 
Create a mini-application named 'AI-TaskMaster' that leverages the 'ai-track' package to manage and execute various AI tasks locally and remotely. This application will serve as a versatile tool for developers and researchers who need to run machine learning models on different devices or cloud services without worrying about the underlying infrastructure. Here's a detailed plan of what your application should do:

1. **Setup**: Begin by setting up a basic Python environment with the necessary dependencies including 'ai-track'. Ensure the application can handle both local and remote inference seamlessly.
2. **Task Management Interface**: Design a simple command-line interface where users can input details of their AI tasks, such as the model name, data source, target device (local or remote), and desired inference settings.
3. **Model Repository Integration**: Integrate with a public model repository or allow users to upload their custom models. Use 'ai-track' to track and manage these models efficiently.
4. **Inference Execution**: Utilize 'ai-track' to execute the specified tasks either locally or remotely based on user preference. Ensure the application supports multiple cloud providers if running tasks remotely.
5. **Results Handling**: After executing the task, process and present the results back to the user through the CLI. Allow options to save the results locally or export them.
6. **Logging & Monitoring**: Implement logging capabilities to keep track of each task execution status and performance metrics. Users should be able to review past executions and monitor ongoing ones.
7. **Security Measures**: Since the application might deal with sensitive data, ensure there are basic security measures in place, such as secure data transfer protocols and authentication mechanisms for accessing remote services.
8. **User Documentation**: Finally, provide comprehensive documentation explaining how to use the application, including setup instructions, examples of common tasks, and troubleshooting tips.

By following these steps, you'll create a powerful yet easy-to-use tool that showcases the capabilities of the 'ai-track' package in managing complex AI workflows.

💬 Discussion Feed

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