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

• URL: http://arxiv.org/abs/2308.01990v4
• Date: Mon, 27 Jan 2025 20:37:59 GMT
• Title: From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?
• Authors: Rodrigo Pedro, Daniel Castro, Paulo Carreira, Nuno Santos,
• Abstract summary: We present a comprehensive examination of P$$ injections targeting web applications based on the Langchain framework. Our findings indicate that LLM-integrated applications based on Langchain are highly susceptible to P$$ injection attacks, warranting the adoption of robust defenses. We propose four effective defense techniques that can be integrated as extensions to the Langchain framework.
• Score: 4.361862281841999
• License:
• Abstract: Large Language Models (LLMs) have found widespread applications in various domains, including web applications, where they facilitate human interaction via chatbots with natural language interfaces. Internally, aided by an LLM-integration middleware such as Langchain, user prompts are translated into SQL queries used by the LLM to provide meaningful responses to users. However, unsanitized user prompts can lead to SQL injection attacks, potentially compromising the security of the database. Despite the growing interest in prompt injection vulnerabilities targeting LLMs, the specific risks of generating SQL injection attacks through prompt injections have not been extensively studied. In this paper, we present a comprehensive examination of prompt-to-SQL (P$_2$SQL) injections targeting web applications based on the Langchain framework. Using Langchain as our case study, we characterize P$_2$SQL injections, exploring their variants and impact on application security through multiple concrete examples. Furthermore, we evaluate 7 state-of-the-art LLMs, demonstrating the pervasiveness of P$_2$SQL attacks across language models. Our findings indicate that LLM-integrated applications based on Langchain are highly susceptible to P$_2$SQL injection attacks, warranting the adoption of robust defenses. To counter these attacks, we propose four effective defense techniques that can be integrated as extensions to the Langchain framework. We validate the defenses through an experimental evaluation with a real-world use case application.

Related papers

• Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs [6.936401700600395]
  Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors, that implement security requirements. This is most likely due to the LLM lacking knowledge about some existing attacks and to the generated code being not evaluated in real usage scenarios. We propose a novel approach integrating Retrieval Augmented Generation (RAG) and Self-Ranking into the LLM pipeline.
  arXiv  Detail & Related papers  (2024-11-27T10:48:37Z)
• FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks [45.65210717380502]
  Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications. prompt injection attacks are particularly threatening, where malicious instructions injected in the external text information can exploit LLMs to generate answers as the attackers desire. This paper introduces a novel test-time defense strategy, named AuThentication with Hash-based tags (FATH)
  arXiv  Detail & Related papers  (2024-10-28T20:02:47Z)
• SecAlign: Defending Against Prompt Injection with Preference Optimization [52.48001255555192]
  Adrial prompts can be injected into external data sources to override the system's intended instruction and execute a malicious instruction. We propose a new defense called SecAlign based on the technique of preference optimization. Our method reduces the success rates of various prompt injections to around 0%, even against attacks much more sophisticated than ones seen during training.
  arXiv  Detail & Related papers  (2024-10-07T19:34:35Z)
• Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context [49.13497493053742]
  This research explores converting a nonsensical suffix attack into a sensible prompt via a situation-driven contextual re-writing. We combine an independent, meaningful adversarial insertion and situations derived from movies to check if this can trick an LLM. Our approach demonstrates that a successful situation-driven attack can be executed on both open-source and proprietary LLMs.
  arXiv  Detail & Related papers  (2024-07-19T19:47:26Z)
• Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models [79.0183835295533]
  We introduce the first benchmark for indirect prompt injection attacks, named BIPIA, to assess the risk of such vulnerabilities. Our analysis identifies two key factors contributing to their success: LLMs' inability to distinguish between informational context and actionable instructions, and their lack of awareness in avoiding the execution of instructions within external content. We propose two novel defense mechanisms-boundary awareness and explicit reminder-to address these vulnerabilities in both black-box and white-box settings.
  arXiv  Detail & Related papers  (2023-12-21T01:08:39Z)
• Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game [86.66627242073724]
  This paper presents a dataset of over 126,000 prompt injection attacks and 46,000 prompt-based "defenses" against prompt injection. To the best of our knowledge, this is currently the largest dataset of human-generated adversarial examples for instruction-following LLMs. We also use the dataset to create a benchmark for resistance to two types of prompt injection, which we refer to as prompt extraction and prompt hijacking.
  arXiv  Detail & Related papers  (2023-11-02T06:13:36Z)
• Formalizing and Benchmarking Prompt Injection Attacks and Defenses [59.57908526441172]
  We propose a framework to formalize prompt injection attacks. Based on our framework, we design a new attack by combining existing ones. Our work provides a common benchmark for quantitatively evaluating future prompt injection attacks and defenses.
  arXiv  Detail & Related papers  (2023-10-19T15:12:09Z)
• Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection [64.67495502772866]
  Large Language Models (LLMs) are increasingly being integrated into various applications. We show how attackers can override original instructions and employed controls using Prompt Injection attacks. We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities.
  arXiv  Detail & Related papers  (2023-02-23T17:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.