🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscations detected. The only concern is the sparse metadata, which slightly raises the risk score.

No network calls detected
Sparse author information

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
Metadata: The author's information is sparse, suggesting potential unreliability, but no concrete evidence of malicious intent.

🔬 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: reading.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ParaChute-UK/simple-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 Simple-Track

Create a real-time video surveillance system using Python and the 'Simple-Track' package. This system will monitor a specified area in a live video feed for any objects that cross a predefined threshold, indicating potential security breaches. The application should capture video from a webcam or a video file, process each frame to track objects, and alert users via visual notifications or audio alarms when an object crosses the set threshold.

Step-by-step instructions:
1. Set up your development environment with Python and install necessary packages including 'Simple-Track', OpenCV for video processing, and Pygame for alerts.
2. Design the main interface of the app which includes a window to display the live video feed and controls to start/stop monitoring and adjust the threshold settings.
3. Implement the video feed capture functionality using OpenCV, ensuring you can switch between webcam input and video files.
4. Integrate 'Simple-Track' into your application to handle object detection and tracking based on user-defined thresholds.
5. Add logic to detect when an object has crossed the defined threshold within the monitored area, triggering alerts.
6. Incorporate visual and auditory alerts using Pygame when a potential breach is detected, such as flashing the screen and playing a sound.
7. Enhance the user experience by allowing users to customize the monitored area and threshold settings dynamically during runtime.
8. Test your application thoroughly under different scenarios to ensure reliability and accuracy in detecting and tracking objects.
9. Document your code and provide instructions on how to install and run the application, including any dependencies and setup steps.