Large Language Models (LLMs) have revolutionized numerous domains, enabling the development of LLM-integrated applications that autonomously plan and act through tool calling. While these applications demonstrate remarkable capabilities, their ability to invoke sensitive operations, such as file system interactions, code execution, and database queries, introduces critical security risks. In particular, prompt injection vulnerabilities, combined with security-sensitive sink functions, can lead to a broad class of attacks we define as \textit{Prompt-to-Anything Injection (P2Xi)}. These vulnerabilities, stemming from the misuse of LLM-generated outputs without proper validation, can result in severe consequences such as Remote Command Execution (RCE), file injection, SQL injection, and Server-Side Request Forgery (SSRF). To address this emerging threat, we propose TaintP2X, a novel static taint analysis framework that models LLM-generated outputs as taint sources, tracks their propagation through sensitive sink functions, and employs LLM-assisted analysis to prune false positives. TaintP2X achieves high precision and scalability, systematically identifying P2Xi vulnerabilities. In evaluations, TaintP2X demonstrated a 77.1% recall on a ground truth dataset of 35 vulnerabilities, outperforming state-of-the-art methods, and uncovered 101 taint paths across 75 GitHub repositories, with several vulnerabilities confirmed by developers. These findings highlight the prevalence and impact of P2Xi vulnerabilities and establish TaintP2X as a practical solution for securing LLM-integrated ecosystems.