AI Analysis
Final verdict: SAFE
The package shows very low risk across all assessed categories with no detected network, shell, obfuscation, or credential risks. The metadata risk is slightly elevated due to the author's single package history but does not indicate malicious intent.
- No network calls
- No shell execution
- No obfuscation
- No credential harvesting
- Single package from author
Per-check LLM notes
- Network: No network calls detected, which is normal for packages that do not require external API interactions during their operation.
- Shell: No shell execution patterns detected, which is expected for pure Python packages without system-level operations.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has only one package, which may indicate a new or less active account, but no other red flags were raised.
Package Quality Overall: Low (3.8/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
Classifier: Typing :: Typed
✦ High
Multiple Contributors
10.0
Active multi-contributor project
6 unique contributor(s) across 100 commits in awslabs/aws-solutions-constructsActive community — 5 or more distinct 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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository awslabs/aws-solutions-constructs appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Amazon Web Services" 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-solutions-constructs.aws-lambda-stepfunctions
Create a fully-functional mini-application that manages the processing of user-uploaded images using AWS Lambda and AWS Step Functions. The application should allow users to upload an image, trigger a series of image processing tasks, and receive a final processed image as output. The application will use the 'aws-solutions-constructs.aws-lambda-stepfunctions' package to define the interaction between the Lambda functions and the Step Function state machine. Step 1: Set up the project environment and install necessary packages including 'aws-solutions-constructs.aws-lambda-stepfunctions'. Step 2: Define the AWS Lambda functions for the following tasks: - Image Upload Handler: Receives the uploaded image and starts the Step Function execution. - Image Resize Handler: Resizes the image to a specified width and height. - Image Filter Handler: Applies a filter (e.g., grayscale, sepia) to the image. - Image Save Handler: Saves the processed image to an S3 bucket. Step 3: Use 'aws-solutions-constructs.aws-lambda-stepfunctions' to create a state machine that orchestrates these Lambda functions. Define states for each handler and specify transitions based on success or failure conditions. Step 4: Implement error handling and retries within the Step Function to manage failures during the image processing pipeline. Step 5: Develop a simple web frontend using Flask to allow users to upload images and view the status of their processing task. Suggested Features: - Provide options to choose between different resize dimensions and filters. - Display real-time status updates of the image processing task on the frontend. - Allow users to download the processed image from the web interface.
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