azure-anomaly-shim

v0.1.0 suspicious
5.0
Medium Risk

A drop in replacement for the retired Azure Anomaly Detector service, powered by PyOD.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network, shell, obfuscation, and credential risks but has a high metadata risk due to suspicious git repository activity and maintainer history. This combination raises concerns about potential supply-chain compromise.

  • High metadata risk
  • Suspicious git repository activity
  • Unclear maintainer history
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 direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: High risk due to suspicious git repository activity and maintainer history.

📦 Package Quality Overall: Low (2.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3284 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 3 commits in Sibonile7/azure-anomaly-shim
  • Single author with few commits — possibly a personal or throwaway project

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Single contributor with only 3 commit(s) — possibly throwaway account
  • All 3 commits happened within 24 hours
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 azure-anomaly-shim 
Create a Python-based mini-application that utilizes the 'azure-anomaly-shim' package to detect anomalies in time-series data. This application will serve as a tool for monitoring and analyzing various types of data streams such as financial transactions, website traffic, sensor readings, etc., providing insights into unusual patterns or behaviors that could indicate issues or opportunities. The application should include the following components:

1. **Data Input Module**: Users should be able to input their own time-series data either through a CSV file upload or direct API integration with data sources like databases or other web services.
2. **Anomaly Detection Engine**: Utilize the 'azure-anomaly-shim' package to process the input data and identify potential anomalies based on statistical methods provided by PyOD. The application should support different anomaly detection models offered by 'azure-anomaly-shim', allowing users to choose the most appropriate one for their specific dataset.
3. **Visualization Module**: Implement a simple yet effective visualization feature that displays the original data alongside the detected anomalies. This could be done using libraries like Matplotlib or Plotly to provide interactive charts and graphs.
4. **Alerting System**: Integrate an alerting mechanism that notifies users via email or SMS when significant anomalies are detected. This feature will help in immediate response to potential issues.
5. **Configuration Interface**: Provide a user-friendly interface where users can configure settings such as sensitivity levels, detection frequency, and preferred alert methods.

The application should be designed with modularity in mind, making it easy to extend functionalities or integrate additional features in the future. Ensure that the codebase is well-documented and includes comments explaining the use of 'azure-anomaly-shim' and how each part of the application contributes to the overall functionality.

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