Metacognition, a crucial aspect of intelligence, enables effective learning and decision-making, and its integration into Large Language Models (LLMs) is essential for developing transparent and capable AI systems. Recent advancements in LLMs have demonstrated significant progress in various tasks, but the extent to which they can exhibit metacognitive capabilities remains unclear. Researchers are working to establish foundations and identify opportunities for metacognition in LLMs, which could lead to more reliable and trustworthy AI systems1. The development of metacognitive LLMs has significant implications for security, as it can reshape both the capability and risk surfaces of these models. As LLMs continue to evolve, understanding their metacognitive capabilities will be crucial for mitigating potential risks and ensuring their safe deployment. The security implications of LLM developments, particularly those from prominent vendors like Meta, trail the hype cycle, emphasizing the need for careful consideration of these systems' capabilities and limitations.