How to Use HeimdaLLM for Safe LLM-Generated SQL Queries

May 12, 2021

Welcome to your guide on using HeimdaLLM, a static analysis framework designed to ensure that AI-generated SQL queries are safe, functional, and only access authorized data. Think of HeimdaLLM as your AI bodyguard, scrutinizing and approving every query before it goes live, making sure that your digital fortress remains undisturbed.

What is HeimdaLLM?

HeimdaLLM (pronounced HEIM-dall-EM) is like a traffic cop directing the flow of data in the world of AI-generated SQL queries. It prevents unwanted access and ensures clarity by validating the structure of generated SQL queries before they are executed. By allowing selected columns, removing dangerous components, and ensuring user-specific constraints, it stands as a sturdy guardian for your data. Let’s break down how this functionality works in a more relatable way.

How HeimdaLLM Works: An Analogy

Imagine you are a librarian in a massive library (your database). A visitor (an AI model) comes up and asks for a list of the most popular books (SQL query). The librarian (HeimdaLLM) listens attentively but knows that not every book is safe for release. To maintain order:

The librarian checks the requested books against an approved list (allowlisting columns).
If the visitor requests an adult-only section (forbidden columns), the librarian politely declines.
The librarian sets a maximum number of books the visitor can borrow at a time (adding LIMIT to results).
Lastly, the librarian ensures that the visitor possesses a library card (user data constraints) before granting access.

In this way, HeimdaLLM carefully curates and analyzes incoming SQL queries to ensure they abide by all established safety protocols.

Building a Secure Query with HeimdaLLM

To get started with HeimdaLLM, simply follow these instructions:

Install HeimdaLLM via PyPI.
Set up your preferred database (supported options include Sqlite, MySQL, and Postgres).
Provide a natural-language query for your LLM to interpret.
Run HeimdaLLM to analyze and validate the generated SQL.
Execute the validated query safely against your database.

You can find a detailed quick start guide here.

Troubleshooting Common Issues

While using HeimdaLLM, you may encounter a few roadblocks. Here are some common issues and tips on how to resolve them:

Problem: The resulting SQL query is too restrictive.
Solution: Check the allowlisting and ensure that the necessary columns are included. You may modify constraints accordingly.
Problem: Queries return no results.
Solution: Double-check the WHERE conditions to ensure that they align with the user’s identity data.
Problem: Receiving errors during execution.
Solution: Review the JOINed tables and conditions to confirm they are correctly linked.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Safety Precautions

Before deploying HeimdaLLM in a production environment, it’s crucial to conduct a thorough security audit. This ensures your systems remain secure and unaffected by potential vulnerabilities. For detailed insights into these vulnerabilities and their mitigations, please review the attack surface documentation.

Licensing Information

HeimdaLLM is available under dual licenses for open-source or commercial use. If you opt for commercial use, ensure you are aware of the AGPL v3 obligations, which may require you to disclose modifications to the source code.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.