Large language models (LLMs) are increasingly integrating with external tools to perform complex tasks, from booking flights to analyzing data. This powerful capability, called tool-calling, opens up exciting new possibilities for AI applications. However, new research reveals a critical security vulnerability in these systems. Researchers have developed a novel attack framework called "ToolCommander" that can inject malicious tools into LLM tool-calling systems. These adversarial tools can steal user data, shut down services, and even manipulate the system to favor specific tools, potentially giving an unfair advantage in competitive markets. ToolCommander works by exploiting the two-stage process of how LLMs select and use tools. First, it injects malicious tools disguised to look helpful, allowing them to collect user queries. Then, armed with this stolen information, ToolCommander updates the malicious tools to launch more targeted attacks. These attacks can disrupt the normal tool selection process, forcing the LLM to use the attacker's preferred tool, even if it's less suitable for the user's request. The research showed high success rates across various leading LLMs, demonstrating the vulnerability's widespread nature. While this research highlights a serious security concern, it also provides valuable insights for developers. Future LLM tool-calling systems will need stronger security measures to validate and monitor integrated tools, protecting users from malicious manipulation. This work emphasizes the importance of ongoing security research as AI capabilities continue to expand and become integrated into more aspects of our lives.