Excessive agency in large language models (LLMs) is a significant security risk that arises when these artificial intelligence systems are endowed with more power and access than necessary, leading to potential unauthorized data access, remote execution, privacy breaches, financial loss, and reputational damage. This vulnerability often results from poorly implemented features where LLMs are given unnecessary permissions to tools, databases, or backend systems, thereby increasing the attack surface. To mitigate these risks, developers should adhere to the principle of least privilege by limiting the capabilities of LLMs to only what is essential for their tasks, implementing strict access controls, and adding safeguards such as human oversight, throttling, and robust monitoring. Continuous security audits, testing for unauthorized access, and monitoring for anomalous behavior are crucial in identifying and preventing excessive agency issues, especially as generative AI applications evolve and become more integral to daily operations.