aws-resource-validator-ce

v2.0.3 suspicious
4.0
Medium Risk

Pydantic v2 models for AWS ce, shipped as a PEP 420 namespace extension of aws-resource-validator.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of malicious activity, but the metadata risk score is elevated due to sparse author information and possibly inactive account status.

  • Sparse author information
  • Possibly inactive account status
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 from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of code obfuscation for malicious purposes.
  • Credentials: No credential harvesting patterns detected, suggesting legitimate usage without the risk of stealing secrets.
  • Metadata: The author's information is sparse and the account seems new or inactive, which raises some suspicion but not enough to conclude malice.

📦 Package Quality Overall: Low (3.8/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Brief PyPI description (285 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 75 commits in CoreOxide/aws_resource_validator
  • Small but multi-author team (3–4 contributors)

🔬 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

Repository CoreOxide/aws_resource_validator 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 aws-resource-validator-ce 
Your task is to create a command-line utility named 'CostExplorerAnalyzer' using Python. This tool will allow users to analyze cost and usage data from their AWS accounts via AWS Cost Explorer API. The utility will leverage the 'aws-resource-validator-ce' package to validate and manage the complex structures required by the AWS Cost Explorer API, ensuring that all requests and responses adhere to the correct schema definitions provided by Pydantic v2 models.

### Core Functionality:
- **Data Retrieval:** Implement a feature that allows users to specify date ranges and metric types (such as 'BLENDED_COST', 'USAGE_QUANTITY') to retrieve specific cost and usage data from AWS Cost Explorer.
- **Validation and Error Handling:** Use 'aws-resource-validator-ce' to validate input parameters before sending them to the AWS API. This includes validating date formats, metric types, and other parameters according to the schemas defined in the package.
- **Output Formatting:** Provide options for users to format the output of the retrieved data, such as CSV, JSON, or tabular formats. Ensure that the formatted data is easy to read and understand.
- **Report Generation:** Enable users to generate reports based on the retrieved data. For example, they could request a report showing the top 5 services by cost over a given period.

### Suggested Features:
- **Filtering Options:** Allow users to filter results based on service names, tags, or specific account IDs.
- **Caching Mechanism:** Implement a simple caching mechanism to store previously fetched data, reducing redundant calls to the AWS API.
- **Interactive Mode:** Develop an interactive mode where users can explore different aspects of their cost data through a series of prompts.
- **Error Logging:** Log errors and invalid inputs for troubleshooting purposes.

### Utilization of 'aws-resource-validator-ce':
- **Schema Validation:** Use the Pydantic v2 models provided by 'aws-resource-validator-ce' to validate all input parameters. This ensures that any data sent to the AWS API conforms to the expected structure, minimizing the risk of API call failures due to malformed requests.
- **Response Parsing:** Apply the same validation principles to parse and validate responses received from the AWS API, ensuring that the returned data matches expected schemas and is correctly interpreted by your application.

This project aims to streamline the process of analyzing AWS cost and usage data, providing a robust and user-friendly interface for both casual users and those requiring more advanced analysis capabilities.

💬 Discussion Feed

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