AI Analysis
Final verdict: SAFE
The package shows low risk indicators across all categories except metadata, where there is a non-secure external link and incomplete author information. These issues do not suggest a supply-chain attack.
- Low network and shell execution risks
- No signs of obfuscation or credential harvesting
- Incomplete metadata and non-secure link
Per-check LLM notes
- Network: No network calls detected, which is normal for a library focused on integration with Salesforce.
- Shell: No shell executions detected, which aligns with the expected behavior of a package designed for Salesforce interaction.
- Obfuscation: The observed pattern is likely a standard method for extending module search paths and does not indicate malicious obfuscation.
- Credentials: No patterns indicative of credential harvesting were detected.
- Metadata: The package has a non-secure external link and the author information is incomplete.
Package Quality Overall: Medium (7.8/10)
✦ High
Test Suite
9.0
Test suite present — 12 test file(s) found
Test runner config found: conftest.py12 test file(s) detected (e.g. conftest.py)
✦ High
Documentation
9.0
Well-documented package
Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-sal1 documentation file(s) (e.g. conf.py)Detailed PyPI description (3931 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Type checker (mypy / pyright / pytype) referenced in project13 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
46 unique contributor(s) across 100 commits in apache/airflowActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 4.0
Found 2 obfuscation pattern(s)
under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache Sunder the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # # Licensed to the Apache
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: airflow.apache.org>
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
Git Repository History
Repository apache/airflow 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 apache-airflow-providers-salesforce
Create a small but powerful data pipeline application using Apache Airflow and the 'apache-airflow-providers-salesforce' package. This application will serve as a bridge between Salesforce and a local database, enabling seamless data transfer and management. Here’s a step-by-step guide on how to build this application: 1. **Project Setup**: Start by setting up your development environment with Python, Apache Airflow, and the 'apache-airflow-providers-salesforce' package installed. Ensure you have access to a Salesforce instance and a local database (e.g., PostgreSQL). 2. **Salesforce Connection**: Use the 'apache-airflow-providers-salesforce' package to establish a secure connection to your Salesforce instance. Configure the necessary credentials and permissions within Airflow. 3. **Data Extraction**: Write DAGs (Directed Acyclic Graphs) that utilize the 'apache-airflow-providers-salesforce' package to extract specific datasets from Salesforce. Focus on retrieving recent sales data, customer information, and any other relevant business intelligence data. 4. **Transformation**: Implement tasks within your DAGs to transform the extracted data into a format suitable for storage in your local database. This may include cleaning data, handling inconsistencies, and converting data types as needed. 5. **Loading Data**: Design a process to load the transformed data into your local database. Ensure the loading process is efficient and handles large volumes of data effectively. 6. **Scheduling and Monitoring**: Set up regular scheduling for your data extraction and loading processes. Use Airflow’s monitoring capabilities to keep track of the status of each task and ensure timely execution. 7. **Error Handling and Logging**: Incorporate robust error handling mechanisms to manage failures during data extraction or loading. Utilize Airflow’s logging capabilities to capture detailed logs for troubleshooting. 8. **Security Measures**: Implement security best practices to protect both the data in transit and at rest. Ensure that sensitive data is encrypted and access controls are properly configured. 9. **User Interface**: Develop a simple user interface that allows users to view the status of the data pipeline, schedule new runs, and view logs. This can be done using Airflow’s web UI or by integrating with external tools like Grafana. 10. **Documentation**: Finally, write comprehensive documentation detailing how to set up and use the application. Include instructions for installing dependencies, configuring connections, and running the DAGs. By following these steps, you’ll create a versatile tool that not only automates the process of moving data from Salesforce to a local database but also provides valuable insights through continuous data analysis.
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