🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network and shell activities, but the metadata suggests a potentially inexperienced maintainer with only one published package and missing classifiers, raising suspicion about its legitimacy and security practices.

Low risk in network and shell activities
Metadata indicates potential inexperience or low effort from the maintainer

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
Metadata: The maintainer has only one package and lacks PyPI classifiers, suggesting potential low effort or inexperience which could indicate risk.

🔬 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: 3lc.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 "3LC" 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 3lc-ultralytics

Create a real-time object detection system using the '3lc-ultralytics' package, which integrates 3LC technology with the powerful Ultralytics YOLO model. Your task is to develop a mini-application that allows users to upload images or videos from their device, and then detects objects within those images or video streams in real-time. The application should also have a feature to save the output video with bounding boxes around detected objects, making it useful for security applications, wildlife monitoring, or any scenario requiring real-time analysis of visual data.

Step-by-Step Instructions:
1. Set up your development environment with Python and install the '3lc-ultralytics' package.
2. Design a user-friendly interface where users can upload files or use their webcam as a source.
3. Implement the core functionality of loading and processing the input using the '3lc-ultralytics' package.
4. Integrate the YOLO model provided by '3lc-ultralytics' to perform object detection on the input.
5. Display the processed output in real-time, showing detected objects with bounding boxes.
6. Add a feature to save the output video or image with annotations.
7. Ensure the application is responsive and efficient, providing smooth performance even with high-resolution inputs.

Suggested Features:
- Option to select different YOLO models available through '3lc-ultralytics' for varying levels of accuracy and speed.
- A gallery of sample images or videos for testing purposes.
- An option to adjust detection confidence thresholds.
- Integration with cloud storage services for saving outputs.
- A live preview window that updates in real-time as the model processes the input.

The '3lc-ultralytics' package is utilized throughout the project to handle the object detection process, leveraging its advanced integration of 3LC technology with the YOLO model to provide accurate and fast results.