AI Analysis
Final verdict: SUSPICIOUS
The package shows no signs of immediate malicious activity such as network risks, shell execution, or credential harvesting. However, the metadata risk score is elevated due to the maintainer's limited history with PyPI, raising suspicion about potential supply-chain attacks.
- Metadata risk score is elevated.
- Maintainer has only one other package.
Per-check LLM notes
- Network: No network calls detected, which is not necessarily suspicious for a package focused on AWS ETL pipelines.
- Shell: No shell execution patterns detected, aligning with expectations for a benign ETL pipeline tool.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity related to stealing secrets or credentials.
- Metadata: The maintainer has only one other package, which could indicate a new or less active account.
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "JoshuaHarris391" 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 acdc_aws_etl_pipeline
Develop a mini-application called 'ACDC Data Migrator' which will utilize the 'acdc_aws_etl_pipeline' Python package to streamline the Extract, Transform, and Load (ETL) process for data migration between different AWS services. This application will serve as a robust tool for businesses looking to move their data from one AWS service to another while ensuring data integrity and consistency. The application should have the following key functionalities: 1. **Data Extraction**: Implement a feature that allows users to extract data from various sources such as Amazon S3, DynamoDB, or RDS. Users should be able to specify the source data format and location. 2. **Data Transformation**: Provide options for transforming the extracted data based on user-defined rules. This could include filtering, aggregating, or converting data types. 3. **Data Loading**: Enable users to load the transformed data into different AWS services like S3, Redshift, or another RDS instance. The application should handle the complexity of loading data efficiently and securely. 4. **Logging and Monitoring**: Include a logging system to track the ETL process. Logs should capture start and end times, success/failure status, and any error messages. 5. **User Interface**: Develop a simple web-based UI where users can input their source and destination details, define transformation rules, and monitor the progress of their ETL jobs. 6. **Security Features**: Ensure that all data transfers and transformations are secure by implementing appropriate authentication and encryption methods. To achieve these functionalities, the 'acdc_aws_etl_pipeline' package will be utilized extensively. Specifically, you'll leverage its built-in connectors for different AWS services, its customizable transformation modules, and its logging capabilities to ensure a smooth and reliable ETL process. Your goal is to create a versatile yet straightforward tool that simplifies complex data migrations for users.