# From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?

**Authors:** Rodrigo Pedro, Daniel Castro, Paulo Carreira, Nuno Santos  
**arXiv:** [2308.01990](https://arxiv.org/abs/2308.01990)  
**Date:** 2023-08-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.01990v4/

## Key points

- Prompt-to-SQL (P2SQL) injections pose significant security risks to Large Language Models (LLMs) integrated into web applications.
- P2SQL attacks involve interactions between the LLM and the database, compromising data consistency, accessing confidential information, or injecting malicious data.
- The study characterizes different attack types using various models within the Langchain framework and proposes four effective defense techniques as extensions to Langchain.
- Experimental validation shows the efficacy of these defense techniques in mitigating specific attacks analyzed in the study.
- Further research is needed to discover new vulnerabilities, propose novel defenses, reduce overheads, automate vulnerability exploration processes, and develop user-friendly frameworks for defending against P2SQL attacks.

## Summary

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This paper focuses on the attack vector of Prompt-to-SQL (P2SQL) injections, which pose significant security risks to Large Language Models (LLMs) integrated into web applications. These attacks involve interactions between the LLM and the database, potentially compromising data consistency, accessing confidential information, or injecting malicious data. The study delves deeper into the feasibility of P2SQL attacks by characterizing different attack types using various models within the Langchain framework. It presents a comprehensive examination of P2SQL injections targeting web applications and proposes four effective defense techniques as extensions to Langchain. Experimental validation showcases their efficacy in mitigating specific attacks analyzed in the study. However, there is room for further research focused on discovering new vulnerabilities, proposing novel defenses, reducing overheads, automating vulnerability exploration processes, and developing user-friendly frameworks for defending against P2SQL attacks. This research contributes valuable insights into safeguarding LLM-integrated web applications from prompt-to-SQL injection threats and emphasizes the importance of implementing robust security measures to protect databases from potential data destruction and confidentiality breaches.