AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network, shell execution, obfuscation, and credential harvesting activities. However, the incomplete author information and limited maintenance history raise concerns about its legitimacy.
- Incomplete author information
- Limited maintainer history with PyPI
Per-check LLM notes
- Network: No network calls detected, which is normal for packages that do not require external API interactions.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author's information is incomplete and the maintainer has limited history with PyPI, which raises some suspicion.
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 (315 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_validatorSmall 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 shortAuthor "" 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-cognito-sync
Create a mini-application named 'CognitoSyncValidator' that leverages the 'aws-resource-validator-cognitosync' package to validate Cognito Sync resources in an AWS environment. This tool will help developers ensure their Cognito Sync configurations meet specific criteria before deployment, enhancing security and compliance. Step 1: Set Up Your Environment - Install Python and necessary libraries including boto3 for AWS interactions and pydantic for data validation. - Ensure you have access to an AWS account with permissions to interact with Cognito Sync. Step 2: Define Validation Rules - Use the 'aws-resource-validator-cognitosync' package to define Pydantic models representing valid Cognito Sync configurations. - These models should encapsulate rules such as required fields, allowed values, and structure requirements. Step 3: Implement Resource Fetching - Write functions to fetch Cognito Sync resources from your AWS account using boto3. - Ensure these functions handle pagination and error handling gracefully. Step 4: Validate Resources Against Models - Develop a mechanism to compare fetched resources against the defined Pydantic models. - Provide feedback on which resources pass validation and which do not, highlighting discrepancies. Suggested Features: - A user-friendly command-line interface for interacting with the validator. - Support for validating multiple Cognito Sync resources at once. - Detailed reports on validation results, including warnings and errors. - Integration with CI/CD pipelines to automatically validate configurations during deployments. How 'aws-resource-validator-cognitosync' Package Is Utilized: - The package provides pre-defined Pydantic models for Cognito Sync resources, streamlining the process of defining validation rules. - By leveraging these models, you can focus on implementing the logic to fetch and validate resources, rather than on the details of data structure validation.
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