aurora-dsql-python-connector

v0.2.7 suspicious
6.0
Medium Risk

A connector that integrates IAM Authentication for connecting Python applications to Amazon Aurora DSQL clusters

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate risk level due to potential credential harvesting and unusual network calls, despite having no shell execution or obfuscation risks.

  • Unusual network call to AmazonTrust
  • High risk of credential harvesting
  • Sparse and suspicious metadata
Per-check LLM notes
  • Network: The network call to AmazonTrust suggests certificate validation but is unusual for a database connector package and could indicate unexpected behavior.
  • Shell: No shell execution patterns detected, indicating low risk in this area.
  • Obfuscation: No signs of code obfuscation or encoding patterns detected.
  • Credentials: Potential risk for credential harvesting as the code snippet attempts to retrieve AWS credentials from environment variables.
  • Metadata: The author's information is sparse and the account seems new or inactive, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 18 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: pyproject.toml
  • Test runner config found: pyproject.toml
  • 18 test file(s) detected (e.g. test_example_preferred.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/awslabs/aurora-dsql-connectors/tree/main/
  • Detailed PyPI description (22651 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 32 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in awslabs/aurora-dsql-connectors
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • try: with urllib.request.urlopen( "https://www.amazontrust.com/reposi
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • } aws_profile = os.getenv("AWS_PROFILE") if aws_profile: config["profil
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: amazon.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository awslabs/aurora-dsql-connectors appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aurora-dsql-python-connector 
Create a Python-based utility named 'AuroraDBManager' that leverages the 'aurora-dsql-python-connector' package to manage Amazon Aurora DSQL databases using IAM authentication. This utility will allow users to perform basic CRUD (Create, Read, Update, Delete) operations on database tables. Additionally, it should include features such as database connection status checks, query execution logging, and support for multiple database schemas.

Step 1: Set up your development environment by installing Python and the required packages including 'aurora-dsql-python-connector'. Configure your AWS credentials and ensure you have access to an Amazon Aurora DSQL cluster.

Step 2: Design the main application class 'AuroraDBManager' which initializes a connection to the Aurora database using IAM authentication through the 'aurora-dsql-python-connector'. Ensure error handling is implemented for connection issues.

Step 3: Implement methods within the 'AuroraDBManager' class to perform CRUD operations on specified tables. Each method should accept parameters like table name, schema name, and specific record details where applicable.

Step 4: Add functionality to check the current connection status of the Aurora database and provide feedback to the user if the connection is active or needs to be re-established.

Step 5: Incorporate logging for all executed queries and their results. This feature will help in auditing and debugging purposes.

Step 6: Extend the utility to handle multiple schemas within the same database cluster. Users should be able to specify the schema when performing operations.

The 'aurora-dsql-python-connector' package will be crucial in establishing secure connections using IAM roles and executing SQL commands against the Aurora DSQL database. Ensure the application is well-documented and includes examples on how to use each feature.

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