argussight

v0.3.0 suspicious
5.0
Medium Risk

An addition to MXCuBEWeb enabling simultaneous video streaming and distributed computer vision tasks.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential obfuscation techniques and the legitimacy of shell command execution needs verification.

  • High obfuscation risk through base64 decoding
  • Shell command execution requires further investigation
Per-check LLM notes
  • Network: No network calls detected, which is low risk.
  • Shell: Execution of shell commands may be legitimate if related to video processing, but requires further investigation into the package's purpose.
  • Obfuscation: The use of base64 decoding on frame data suggests potential obfuscation or data hiding, raising suspicion.
  • Credentials: No clear patterns indicative of credential harvesting have been identified.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. test_client.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://mxcube.github.io/argussight/
  • Detailed PyPI description (5595 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

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

Limited contributor diversity

  • 2 unique contributor(s) across 90 commits in mxcube/argussight
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • frame["frame"] = base64.b64decode(frame["data"]) self.add_to_iterable(frame)
  • ) self.copy_frame(base64.b64decode(frame["data"]), frame["size"]) if self._current_fram
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • self._video_stream_process = subprocess.Popen( [ "video-stream
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: esrf.fr

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository mxcube/argussight appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Yan Walesch" 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 argussight 
Create a real-time surveillance system using the 'argussight' package, which integrates seamlessly with MXCuBEWeb to handle both video streaming and distributed computer vision tasks. This system will monitor a specific area (such as a room or outdoor space) and provide alerts based on detected motion or anomalies. Here’s a step-by-step guide to building this system:

1. **Setup Environment**: Ensure you have Python installed along with the necessary libraries including 'argussight'. Install 'argussight' using pip if it's not already installed.
2. **Video Stream Initialization**: Use 'argussight' to initialize a video stream from a camera source. This could be a local webcam or a network camera feed.
3. **Distributed Computer Vision Tasks**: Implement motion detection using 'argussight'. The package allows for efficient processing of video frames in real-time. Consider also integrating face detection or object recognition capabilities.
4. **Alert System**: Set up an alert mechanism that triggers when significant movement or specific objects are detected. Alerts could be sent via email, SMS, or displayed on a dashboard.
5. **User Interface**: Develop a simple web-based interface using Flask or Django to display live video feeds and alerts. Users should be able to view the current status and receive notifications.
6. **Configuration Options**: Allow users to customize settings such as sensitivity levels for motion detection, types of objects to track, and notification preferences.
7. **Testing & Deployment**: Thoroughly test the system under various conditions to ensure reliability. Once tested, deploy the system to a server or cloud environment where it can be accessed remotely.

By following these steps, you'll create a functional surveillance system that leverages the powerful capabilities of 'argussight' to enhance security and monitoring.

💬 Discussion Feed

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