AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity with low risks across all assessed categories. The metadata risk is slightly elevated due to the author having only one package, but it's not indicative of a supply-chain attack.
- No network calls detected
- Single package from author
Per-check LLM notes
- Network: No network calls detected, which is normal for a package that likely handles AWS interactions through the SDK rather than direct HTTP requests.
- Shell: No shell execution patterns detected, which is expected as a PyPI package does not typically require or execute shell commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting no immediate risk of unauthorized credential access.
- Metadata: The author has only one package, which might indicate a new or less active account, but there are no other suspicious flags.
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-kinesis-streams
Create a real-time data processing application using Python and the 'aws-solutions-constructs.aws-lambda-kinesis-streams' package. This application will simulate a scenario where sensors in a smart city collect environmental data such as temperature, humidity, and air quality index (AQI), which is then streamed into an Amazon Kinesis Data Stream. An AWS Lambda function will process this stream data in real-time, detecting anomalies or significant changes in the environmental conditions, and trigger alerts when necessary. Steps to complete the project: 1. Set up your development environment with the necessary AWS SDK and 'aws-solutions-constructs.aws-lambda-kinesis-streams' package installed. 2. Design your Kinesis Data Stream, specifying the number of shards based on expected throughput. 3. Implement a mock sensor data generator that simulates real-time data collection and sends it to the Kinesis Data Stream. 4. Use 'aws-solutions-constructs.aws-lambda-kinesis-streams' to define the interaction between your AWS Lambda function and the Kinesis Data Stream. Ensure the Lambda function processes each record in the stream, analyzing the data for any anomalies or thresholds being crossed. 5. Integrate alerting mechanisms within the Lambda function, such as sending emails or SMS notifications through AWS SNS, whenever abnormal readings are detected. 6. Test your application thoroughly, ensuring that data from the mock sensors is correctly processed by the Lambda function and appropriate alerts are triggered under specific conditions. 7. Document your setup process, including any challenges faced and how they were resolved. Suggested Features: - Implement logging within the Lambda function to track processed records and actions taken. - Allow configuration of threshold values for anomaly detection via environment variables or a simple configuration file. - Enhance alerting by integrating with other AWS services like AWS Lambda Destinations for more flexible notification routing.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue