AI Analysis
Final verdict: SUSPICIOUS
The package has low individual risk scores across network, shell, obfuscation, and credential risks. However, the metadata risk score is moderately high due to incomplete author details and potential inactivity of the author, raising suspicion.
- Incomplete author details
- Potential inactivity of the author
Per-check LLM notes
- Network: No network calls suggest normal behavior for a utility package.
- Shell: No shell execution patterns indicate the package is not attempting to run external commands.
- Obfuscation: No obfuscation patterns detected, suggesting legitimate code practices.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
- Metadata: The author details are incomplete and the author seems to be new or inactive, which raises some suspicion but not enough to conclude 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-medialive
Create a mini-application called 'MediLiveInspector' that helps AWS MediaLive users validate their resource configurations before deploying them. This application will utilize the 'aws-resource-validator-medialive' package to ensure that all provided configurations adhere to the correct structure and constraints defined by AWS MediaLive. Step 1: Define the Application Scope - MediLiveInspector will accept a JSON configuration file representing a MediaLive channel or input. - It will validate this configuration against the Pydantic models provided by 'aws-resource-validator-medialive'. Step 2: Setup the Project Environment - Use Python 3.8+. - Install 'aws-resource-validator-medialive' and any necessary dependencies. Step 3: Implement Configuration Validation - Create a function that reads a JSON file and converts it into a Python dictionary. - Use the Pydantic models from 'aws-resource-validator-medialive' to validate this dictionary. - Provide feedback to the user if the configuration is valid or not, and why. Suggested Features: - Option to output validation results to a log file. - Ability to validate multiple configurations at once. - Command-line interface for easy usage. - Integration with AWS SDKs for direct resource creation if the configuration passes validation. The 'aws-resource-validator-medialive' package is crucial here because it provides the necessary Pydantic models that reflect the schema and requirements of AWS MediaLive resources. By using these models, MediLiveInspector ensures that configurations are not only syntactically correct but also semantically valid according to AWS MediaLive's specifications.
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