awslabs.aurora-dsql-mcp-server

v1.0.31 suspicious
6.0
Medium Risk

An AWS Labs Model Context Protocol (MCP) server for Aurora DSQL

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential credential harvesting via SQL commands and significant obfuscation, despite legitimate maintainer metadata and minimal network/shell risks.

  • High credential risk
  • Moderate obfuscation risk
Per-check LLM notes
  • Network: The presence of network calls is expected for a package that likely interacts with AWS Aurora Database Service.
  • Shell: No shell execution patterns detected, which is normal and indicates no direct system command execution from the package.
  • Obfuscation: The obfuscation pattern detected is not inherently malicious but can be used to hide code, potentially for malicious purposes.
  • Credentials: The SQL commands suggest potential attempts at file access and data exfiltration, indicating high risk of credential harvesting.
  • Metadata: The maintainers appear to be legitimate AWS employees and there are no suspicious links or domains.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/aurora-dsql-mcp-server
  • Detailed PyPI description (15132 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 5.0

Partial type annotation coverage

  • 28 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 42 unique contributor(s) across 100 commits in awslabs/mcp
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • try: async with httpx.AsyncClient(timeout=knowledge_timeout) as client: response =
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • amespace packages. __path__ = __import__('pkgutil').extend_path(__path__, __name__) # Copyright Amazon.com, In
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • [ "SELECT load_file('/etc/passwd')", "SELECT * INTO OUTFILE '/tmp/dump.csv' FROM use
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository awslabs/mcp appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Amazon Web Services, Ram Dwivedula, Yoni Shalom" 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 awslabs.aurora-dsql-mcp-server 
Your task is to create a mini-application that leverages the 'awslabs.aurora-dsql-mcp-server' package to manage and query data stored in an Amazon Aurora database using the Data SQL (DSQL) protocol. This application will serve as a lightweight tool for developers and database administrators to perform common database operations such as querying, inserting, updating, and deleting records from their Aurora databases.

### Project Scope:
- **Database Connection Management**: Implement a secure and efficient connection management system that allows users to connect to their Aurora database instances.
- **Query Execution**: Provide a user-friendly interface for executing SQL queries against the connected database. Results should be displayed in a readable format.
- **Data Manipulation Operations**: Allow users to insert, update, and delete records within the database through the application.
- **Transaction Support**: Ensure that all database operations support transactions, allowing users to commit or rollback changes as needed.
- **Logging and Error Handling**: Implement robust logging and error handling mechanisms to track operations and handle exceptions gracefully.

### Core Features:
1. **User Authentication**: Integrate basic authentication to ensure only authorized users can access the database.
2. **Interactive Query Interface**: Develop an interactive console or web-based interface where users can input SQL queries directly.
3. **CRUD Operations**: Enable users to perform Create, Read, Update, and Delete operations on database tables.
4. **Database Schema Visualization**: Provide a feature to visualize the schema of the connected database, showing tables, columns, and relationships.
5. **Export/Import Data**: Offer options to export query results into CSV or Excel files, and import data from these formats back into the database.
6. **Real-time Monitoring**: Implement real-time monitoring of ongoing database operations and provide performance metrics.

### Utilization of 'awslabs.aurora-dsql-mcp-server':
- Use the package to establish a secure connection to the Aurora database instance using the Model Context Protocol (MCP).
- Leverage the package's capabilities to execute DSQL commands efficiently and securely.
- Integrate the package’s transaction management features to ensure data integrity during operations.
- Utilize the package’s error handling and logging functionalities to enhance the reliability and maintainability of your application.

### Deliverables:
- A fully functional mini-application with a user-friendly interface.
- Detailed documentation explaining how to set up and use the application.
- Source code with comments and explanations for each major component.
- A demo video showcasing key features of the application.

This project aims to demonstrate the power and flexibility of the 'awslabs.aurora-dsql-mcp-server' package while providing a practical tool for database management.

💬 Discussion Feed

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