argus-node

v0.2.2 suspicious
5.0
Medium Risk

Distributed camera node runtime

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks associated with network communication and low maintainer activity, which raises concerns about its reliability and potential for unknown vulnerabilities.

  • moderate network risk due to external communications
  • low maintainer activity and poor metadata quality
Per-check LLM notes
  • Network: The network calls suggest the package communicates with an external server for registration, status updates, and possibly monitoring purposes.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting the package does not pose a threat in terms of stealing secrets or credentials.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_camera.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (994 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

  • 18 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 6.0

Found 4 network call pattern(s)

  • r_uri}/status" resp = requests.get(url) if resp.status_code == 200 and resp.json()["sta
  • ding/register" resp = requests.post(url, json={"pod_id": pod_id}) if resp.status_code !=
  • on/update_pod" resp = requests.post(url, json={"pod_id": pod_id, "pod_state": status}) e
  • on/start_time" resp = requests.get(url) extra = {"node_id": node_id} if node_id is not
✓ 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: leeds.ac.uk>

✓ 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 argus-node 
Create a real-time distributed surveillance system using the 'argus-node' package. This mini-project will allow users to set up multiple camera nodes across different locations, stream video feeds to a central server, and monitor them in real-time. Here’s a step-by-step guide on how to approach building this application:

1. **Setup**: Begin by installing 'argus-node' and setting up your environment. Ensure you have the necessary hardware (cameras) and software (server setup).
2. **Camera Nodes Configuration**: Configure each camera node to capture video streams and send them to the central server. Use 'argus-node' functionalities to handle the distribution of these nodes.
3. **Central Server Development**: Develop a central server application that receives video streams from all nodes. Implement functionality to manage multiple connections, handle data efficiently, and ensure minimal latency.
4. **Real-Time Monitoring Interface**: Create a web-based interface where users can log in and view live video feeds from any connected camera node. Include features like zooming, panning, and adjusting playback speed.
5. **Security Features**: Implement security measures such as user authentication, secure data transmission, and access control. Ensure that only authorized personnel can view or manipulate the camera feeds.
6. **Alert System**: Integrate an alert system that triggers notifications based on predefined conditions (e.g., motion detection). Users should receive alerts via email or SMS.
7. **Scalability and Performance Optimization**: Focus on making the system scalable so it can handle an increasing number of camera nodes without compromising performance. Optimize data transfer and processing methods.
8. **Testing and Deployment**: Thoroughly test the system under various scenarios to ensure reliability and robustness. Deploy the application in a real-world environment and gather feedback for further improvements.

By utilizing 'argus-node', your application will leverage its distributed runtime capabilities to efficiently manage and process video streams from multiple sources. This project not only demonstrates practical use of 'argus-node' but also provides valuable insights into developing real-time monitoring systems.

💬 Discussion Feed

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