AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network usage, shell execution, obfuscation, and credential handling. However, the incomplete author information and potentially inactive account increase the suspicion level, suggesting a need for further investigation.
- Incomplete author information
- Potentially inactive author account
Per-check LLM notes
- Network: No network calls detected, which is unusual for a tool interacting with AWS services but may be acceptable if the package relies solely on local configurations or cached data.
- Shell: No shell execution patterns detected, indicating that the package does not execute external commands which reduces the risk of arbitrary code execution.
- 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 account seems new or inactive, raising some suspicion but not conclusive evidence of 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 (345 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-workspaces-thin-client
Develop a command-line utility named 'WorkspaceValidator' that leverages the 'aws-resource-validator-workspaces-thin-client' Python package to validate AWS WorkSpaces Thin Client configurations. This utility will enable system administrators to ensure their WorkSpaces Thin Clients adhere to specific compliance standards or organizational policies. Step 1: Define the Project Structure - Create a virtual environment for your project. - Set up a Git repository for version control. - Initialize a new Python project using 'poetry' or 'pipenv'. Step 2: Install Dependencies - Install 'aws-resource-validator-workspaces-thin-client' via pip. - Include other necessary libraries such as 'boto3' for AWS SDK, 'click' for command-line interface, and 'pydantic' for data validation. Step 3: Implement Core Functionality - Use the Pydantic models provided by 'aws-resource-validator-workspaces-thin-client' to define schemas for validating WorkSpaces Thin Client configurations. - Write functions to fetch WorkSpaces Thin Client configurations from AWS using 'boto3'. - Implement validation logic that checks if the fetched configurations match the defined schemas. Step 4: Design User Interface - Utilize 'click' to create a user-friendly CLI with commands like 'validate', 'list-configs', and 'check-compliance'. - Ensure the CLI provides clear output messages indicating whether configurations comply with the specified standards. Step 5: Add Additional Features - Include an option for users to specify custom validation rules. - Allow the utility to generate reports detailing non-compliant configurations. - Implement error handling to manage issues such as invalid AWS credentials or network errors. Step 6: Testing and Documentation - Write unit tests for all core functionalities using 'pytest'. - Document the project setup, usage, and configuration details in a README.md file. - Ensure the project is well-documented and easy to understand for future contributors or users.
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