aws-resource-validator-backupsearch

v2.0.3 suspicious
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no signs of malicious activities such as network calls, shell executions, or credential harvesting. However, the metadata risk score is elevated due to missing or short author information and potential inactivity, raising concerns about its legitimacy.

  • metadata risk due to missing or short author information
  • potential inactivity of the maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external API interactions.
  • Shell: No shell execution patterns detected, which is expected and indicates no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting that the package is not likely engaged in unauthorized credential collection.
  • Metadata: The maintainer's author name is missing or very short and appears to be new or inactive, 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_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-backupsearch 
Create a Python-based command-line utility named 'BackupSearchAnalyzer' that leverages the 'aws-resource-validator-backupsearch' package to validate and analyze AWS resource backups. This utility should allow users to input specific AWS resource identifiers and then perform validation checks against the provided Pydantic v2 models. Additionally, the tool should be capable of searching through backup history and providing detailed reports on the status and frequency of backups for specified resources. Here are the key steps and features to include:

1. **Setup and Initialization**: Begin by installing the necessary dependencies including 'aws-resource-validator-backupsearch', boto3 for AWS interactions, and any other required libraries. Ensure your AWS credentials are configured correctly for the application to access the necessary services.

2. **Input Handling**: Implement a command-line interface that accepts user inputs for AWS resource identifiers (such as EC2 instance IDs, RDS DB instance identifiers, etc.). Validate these inputs using the Pydantic models from 'aws-resource-validator-backupsearch'.

3. **Backup Validation**: Use the validated inputs to query AWS Backup for the current backup plan and execution details associated with the specified resources. Apply the Pydantic models to validate the returned data against expected schemas.

4. **Historical Backup Analysis**: Extend the functionality to allow users to request historical backup data over a specified period. Utilize the Pydantic models to ensure the integrity of the data retrieved from AWS Backup.

5. **Report Generation**: Develop a feature that generates comprehensive reports based on the analyzed data. These reports should include information such as the last successful backup date, total number of backups performed within a given timeframe, and any anomalies detected during the validation process.

6. **Output Presentation**: Finally, present the report either in the console output or save it as a file in formats like CSV or JSON, depending on user preference. Ensure the output is human-readable and easy to understand.

This project aims to provide a robust solution for managing and validating AWS resource backups, making use of the structured validation capabilities offered by the 'aws-resource-validator-backupsearch' package.

💬 Discussion Feed

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