aws-resource-validator-redshift-data

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious intent, but the metadata risk score due to the new or inactive author account and lack of proper author identification warrants further investigation.

  • Metadata risk due to new or inactive author account
  • Lack of proper author identification
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting the package is not engaging in suspicious activities regarding secret or credential handling.
  • Metadata: The author has a new or inactive account and lacks a proper author name, which may indicate a less experienced or potentially suspicious maintainer.

📦 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 (318 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-redshift-data 
Develop a Python-based utility called 'RedshiftQueryAnalyzer' that leverages the 'aws-resource-validator-redshift-data' package to validate and execute SQL queries on Amazon Redshift clusters. This utility will serve as a powerful tool for database administrators and developers who need to ensure their SQL scripts are correctly formatted and executed against Redshift clusters without manual intervention. Here’s a step-by-step guide on how to build this utility:

1. **Setup**: Begin by setting up your development environment with Python 3.x installed. Ensure you have access to AWS credentials with permissions to interact with Redshift Data API. Install the necessary packages including 'aws-resource-validator-redshift-data', boto3 (AWS SDK for Python), and pydantic.

2. **Model Validation**: Utilize the Pydantic models provided by 'aws-resource-validator-redshift-data' to define a schema for validating SQL query inputs. These models should help ensure that the queries adhere to specific standards required for execution on Redshift.

3. **Query Execution**: Implement functionality to connect to an Amazon Redshift cluster using the Redshift Data API through boto3. Once connected, validate the incoming SQL query against the defined Pydantic model before executing it on the cluster. Handle exceptions gracefully and provide meaningful error messages if validation fails or execution encounters issues.

4. **Result Handling**: After successful execution, manage the results appropriately. For SELECT statements, return the fetched data in a readable format such as JSON or CSV. For other types of queries (INSERT, UPDATE, DELETE), confirm the operation's success and provide relevant feedback.

5. **Logging & Monitoring**: Integrate logging to capture important events like query validation errors, execution successes/failures, and any exceptions. Optionally, extend the utility to support monitoring these logs via a simple web interface or by sending notifications through email/SMS when critical errors occur.

6. **Security Considerations**: Since this utility interacts with sensitive data stored in Redshift, ensure proper security measures are in place. Use encrypted connections, limit permissions strictly based on the principle of least privilege, and consider implementing additional layers of authentication beyond just AWS IAM roles.

7. **Documentation & Testing**: Finally, document all aspects of your utility including setup instructions, usage examples, and troubleshooting tips. Write comprehensive tests covering various scenarios to ensure reliability and robustness of the application.

By following these steps, you'll create a versatile and secure utility that not only simplifies the process of working with Redshift but also enhances the overall quality and reliability of database operations.

💬 Discussion Feed

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