AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network calls, shell execution, obfuscation, and credential handling. However, the metadata risk score is elevated due to missing maintainer information and potential inactivity, which makes it suspicious.
- Missing maintainer's author name
- Potential inactivity of the maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external API interactions.
- Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer's author name is missing and they appear to be new or inactive, which raises some suspicion but not enough to conclusively determine 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 (306 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-s3vectors
Your task is to develop a Python-based utility called 'S3VectorInspector' that leverages the 'aws-resource-validator-s3vectors' package to validate and manage S3 vectors resources in Amazon Web Services. This utility will serve as a powerful tool for developers and system administrators who work extensively with AWS S3 vectors, providing them with a streamlined way to ensure their resources are correctly configured and optimized. The core functionalities of 'S3VectorInspector' include: - Validation of S3 vector resource configurations against predefined schemas provided by the 'aws-resource-validator-s3vectors' package. - Reporting on any discrepancies found during validation, offering suggestions for corrections where possible. - Offering an option to automatically correct minor issues based on user preferences. - Providing detailed insights into the state of S3 vector resources, including performance metrics and usage statistics. To begin, you'll need to install the 'aws-resource-validator-s3vectors' package using pip. Once installed, you should create a set of functions within your utility that utilize the Pydantic models from this package to validate different aspects of S3 vector resources. For example, you might have a function named 'validate_s3_vector_config' that takes a configuration file as input and returns a report detailing whether the configuration adheres to the standards outlined by the package. Additionally, consider implementing a feature that allows users to upload their S3 vector configurations directly through the command line interface of your utility. This feature should then use the 'aws-resource-validator-s3vectors' package to validate these configurations and output the results in an easily digestible format. For advanced users, include an option to schedule regular checks of S3 vector resources, ensuring that they remain compliant with best practices over time. This could involve setting up periodic validations and sending alerts if any issues are detected. Remember to document each function clearly, explaining its purpose and how it utilizes the 'aws-resource-validator-s3vectors' package. Also, provide examples of valid and invalid S3 vector configurations to help users understand the expected structure and common pitfalls.
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