Imagine a piece of malicious software, constantly changing its form, making it nearly impossible for traditional cybersecurity tools to detect. This isn't science fiction—it's the potential of AI-powered code mutation. Recent research explores how Large Language Models (LLMs), the same technology behind ChatGPT, can be fine-tuned to rewrite code while preserving its functionality. This means malware could dynamically alter its structure, evading signature-based detection systems that rely on identifying known patterns. The research introduces a novel 'code mutation training' technique that focuses on modifying code at the subroutine level. This approach allows for more manageable and verifiable changes, ensuring the mutated code remains functional while becoming virtually unrecognizable. Experiments with a lightweight LLM demonstrate the effectiveness of this technique, highlighting a significant increase in code variation after training. While this research unlocks powerful possibilities for software development, it also reveals a potential new era of cyber threats. As LLMs become more accessible, the ability to create sophisticated, ever-evolving malware could become a serious security challenge. The research underscores the urgent need for next-generation cybersecurity tools that can adapt to this evolving threat landscape, moving beyond static analysis and embracing dynamic, behavior-based detection methods. The future of cybersecurity hinges on staying ahead of these AI-driven threats.