The text discusses the discovery of a remote code execution vulnerability in the Vanna.AI library, which provides a text-to-SQL interface utilizing large language models (LLMs). This vulnerability, identified as CVE-2024-5565, arises from a prompt injection attack that can bypass pre-prompting safeguards. These attacks exploit the inherent weakness in LLMs where user inputs can manipulate the context and override predefined instructions. The document highlights the difference between isolated and integrated prompt injections, with the latter posing significant security risks when LLMs are connected to actionable systems. In the case of Vanna.AI, the integration of LLMs with SQL servers and dynamic code execution led to the vulnerability, allowing for arbitrary Python code execution via manipulated prompts. The text emphasizes the need for robust security measures beyond pre-prompting to protect against such vulnerabilities, suggesting solutions like additional prompt injection tracing models, output integrity checks, and sandboxing. Finally, it highlights the role of JFrog Security in identifying and mitigating vulnerabilities in open-source technologies, advocating for a secure approach to integrating LLMs in applications.