  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.
