apialerts

v1.2.0 suspicious
4.0
Medium Risk

Python wrapper for the API Alerts service

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, obfuscation, and credential activities. However, the incomplete maintainer's profile and potential inactivity raise concerns about its origin and maintenance, leading to a cautious stance.

  • Incomplete maintainer profile
  • Potential inactivity of the maintainer
Per-check LLM notes
  • Network: The network calls appear to be standard HTTP requests and don't inherently suggest malicious activity.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has an incomplete profile and appears to be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.4/10)

◈ 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://apialerts.com/docs/sdks/python
  • Detailed PyPI description (4866 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 15 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 22 commits in apialerts/apialerts-python
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • ()).encode('utf-8') req = urllib.request.Request( url=base_url, data=body, me
  • , ) try: with urllib.request.urlopen(req, timeout=_TIMEOUT_SECONDS) as response:
  • None: with patch('urllib.request.urlopen', return_value=_success_response()):
  • None: with patch('urllib.request.urlopen', return_value=_success_response('W', 'C')):
  • None: with patch('urllib.request.urlopen', return_value=_success_response(warnings=['deprecat
  • None: with patch('urllib.request.urlopen', side_effect=_http_error(400)): cli
✓ 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: apialerts.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository apialerts/apialerts-python 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 apialerts 
Create a real-time alert system using the 'apialerts' Python package. This mini-app will allow users to monitor specific APIs for changes or errors and receive instant notifications via email or SMS when certain conditions are met. Here's a step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed and create a virtual environment for your project.

2. **Install Required Packages**: Install the 'apialerts' package alongside other necessary packages like 'requests' for making HTTP requests and 'twilio' for sending SMS alerts.

3. **API Configuration**: Configure the APIs you want to monitor. For each API, define the endpoint, expected response status codes, and any error conditions that should trigger an alert.

4. **Alerts Setup**: Use 'apialerts' to set up alert rules based on the configurations from step 3. Define how often the system should check the APIs and under what conditions an alert should be triggered.

5. **Notification Integration**: Integrate email and SMS notification services. For emails, use Python's built-in 'smtplib'. For SMS, use the Twilio API.

6. **User Interface**: Develop a simple web interface using Flask or Django where users can add, modify, or delete API monitoring rules and view alert history.

7. **Testing and Deployment**: Thoroughly test your application to ensure it correctly monitors APIs and sends alerts. Deploy your application to a cloud service provider like AWS or Heroku.

Features:
- Real-time monitoring of multiple APIs
- Customizable alert conditions
- Email and SMS notifications
- User-friendly web interface for management

By following these steps and utilizing the 'apialerts' package effectively, you'll create a robust and scalable alert system capable of notifying users instantly about critical issues with their APIs.

💬 Discussion Feed

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