AI Analysis
Final verdict: SUSPICIOUS
The package exhibits low individual risks but raises concerns due to incomplete author information and a single package from the maintainer, suggesting potential supply-chain attack indicators.
- Incomplete author information
- Single package from 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 no direct system command execution from the package.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author information is incomplete and the maintainer has only one package, which may indicate a less established or potentially suspicious account.
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 (321 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-ssm-guiconnect
Create a Python-based command-line utility named 'SSMGuiConnectValidator' that leverages the 'aws-resource-validator-ssm-guiconnect' package to validate AWS SSM GuiConnect resources. This utility should allow users to input or upload a configuration file containing details of their SSM GuiConnect resources, which the utility will then parse and validate against predefined Pydantic models provided by the package. The validation process should ensure that all necessary fields are present, correctly formatted, and adhere to AWS's schema requirements for SSM GuiConnect resources. Key Features: 1. Support for both manual input and file upload for resource configurations. 2. Integration with the 'aws-resource-validator-ssm-guiconnect' package to perform validation checks using Pydantic models. 3. Detailed error reporting for any validation failures, indicating specific issues with individual resources. 4. An option to automatically correct minor errors such as formatting issues, if possible. 5. A clean, user-friendly CLI interface for ease of use. 6. Optional integration with AWS SDK for Python (boto3) to directly validate resources against AWS. Steps to Build: 1. Set up your development environment with Python and install the 'aws-resource-validator-ssm-guiconnect' package along with other necessary dependencies like Pydantic and boto3. 2. Define the CLI structure and commands for handling input and output operations. 3. Implement functionality to read and parse input files or manual inputs into Python objects. 4. Utilize the 'aws-resource-validator-ssm-guiconnect' package to create validators based on the provided Pydantic models. 5. Develop the validation logic to check each resource against these validators, providing feedback on any discrepancies. 6. Integrate error handling to manage exceptions gracefully and provide meaningful error messages. 7. Optionally, extend the utility to interact with AWS services via boto3 for real-time validation. 8. Test the utility thoroughly with various configurations and edge cases to ensure robustness. 9. Document the usage instructions and API documentation for users and future contributors.
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