AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity with low scores across all categories. The metadata risk is slightly elevated due to the author having only one package, but this alone does not suggest any supply-chain attack.
- No network calls detected.
- No shell execution patterns detected.
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 normal and transparent code practices.
- Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
- Metadata: The author has only one package, which may indicate a new or less active account, but no other red flags were detected.
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-fargate-kinesisstreams
Create a real-time data processing application using the 'aws-solutions-constructs.aws-fargate-kinesisstreams' package in Python. This application will stream IoT device data into a Kinesis Data Stream, where it will be processed in real-time by a Fargate service. The application should include the following components: 1. **Data Source Simulation**: Simulate IoT devices sending temperature and humidity readings every few seconds. 2. **Kinesis Data Stream**: Set up a Kinesis Data Stream to ingest the simulated data. 3. **Fargate Service**: Deploy a Fargate service that consumes data from the Kinesis Data Stream, processes the data (e.g., calculate average temperatures over time), and stores the results in a DynamoDB table. 4. **Visualization**: Implement a simple visualization component (using a library like Plotly or Matplotlib) to display the processed data in real-time. 5. **Monitoring**: Add basic monitoring capabilities to track the performance of the Fargate service and the health of the Kinesis Data Stream. The application should demonstrate how to utilize the 'aws-solutions-constructs.aws-fargate-kinesisstreams' package effectively by integrating it within a larger system architecture. Specifically, focus on setting up the Fargate service to consume from the Kinesis Data Stream, ensuring efficient data processing and storage. Additionally, explore how to handle potential challenges such as scaling the Fargate service based on incoming data volume and maintaining data integrity across the processing pipeline.
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