AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in network, shell, and obfuscation areas but has a moderate metadata risk due to sparse author information and potentially inactive account.
- Metadata risk of 3/10
- Sparse author information and possibly inactive account
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating no direct system command execution activities.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting the package does not pose a threat in terms of stealing secrets or credentials.
- Metadata: The author's information is sparse and the account seems new or inactive, raising some concerns but not definitive 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 (294 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-wafv2
Create a Python-based web application that validates AWS WAFv2 resources using the 'aws-resource-validator-wafv2' package. This application should allow users to upload their AWS WAFv2 configuration files and receive validation feedback on whether the configurations adhere to best practices and AWS guidelines. Here’s a detailed plan for your project: 1. **Setup Environment**: Begin by setting up a virtual environment and installing necessary packages including 'aws-resource-validator-wafv2', Flask for the web framework, and any other dependencies. 2. **Model Definition**: Use 'aws-resource-validator-wafv2' to define Pydantic models that represent various AWS WAFv2 resource types such as WebACLs, Rules, RuleGroups, etc. 3. **Validation Logic**: Implement logic within your application that reads the uploaded configuration file, parses it into the defined Pydantic models, and then runs validations against these models. Ensure that you handle common issues like missing required fields, invalid formats, and unsupported configurations. 4. **User Interface**: Develop a simple yet effective user interface using Flask where users can upload their configuration files. Upon submission, the application should display a summary of the validation results, highlighting any issues found. 5. **Error Reporting**: Design a system for reporting errors in a user-friendly manner. Include explanations for each error and suggestions on how to correct them. 6. **Testing**: Thoroughly test your application with various AWS WAFv2 configurations to ensure robustness and accuracy. 7. **Deployment**: Once validated, deploy your application either locally or to a cloud service provider. This project will not only serve as a useful tool for AWS administrators but also as a practical example of integrating Pydantic models and AWS services in a real-world application.
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