The rapid advancement of large language models (LLMs) has enabled the ability to effectively analyze and generate code nearly instantaneously, resulting in their widespread adoption in software development. Following this advancement, researchers and companies have begun integrating LLMs across the hardware design and verification process. However, these highly potent LLMs can also induce new attack scenarios upon security vulnerabilities across the hardware development process. One such attack vector that has not been explored is intellectual property (IP) piracy. Given that this attack can manifest as rewriting hardware designs to evade piracy detection, it is essential to thoroughly evaluate LLM capabilities in performing this task and assess the mitigation abilities of current IP piracy detection tools. Therefore, in this work, we propose LLMPirate, the first LLM-based technique able to generate pirated variations of circuit designs that successfully evade detection across multiple state-of-the-art piracy detection tools. We devise three solutions to overcome challenges related to integration of LLMs for hardware circuit designs, scalability to large circuits, and effectiveness, resulting in an end-to-end automated, efficient, and practical formulation. We perform an extensive experimental evaluation of LLMPirate using eight LLMs of varying sizes and capabilities and assess their performance in pirating various circuit designs against four state-of-the-art, widely-used piracy detection tools. Our experiments demonstrate that LLMPirate is able to consistently evade detection on 100% of tested circuits across every detection tool. Additionally, we showcase the ramifications of LLMPirate using case studies on IBEX and MOR1KX processors and a GPS module, that we successfully pirate. We envision that our work motivates and fosters the development of better IP piracy detection tools.

翻译：大语言模型（LLM）的快速发展使其能够近乎即时地有效分析和生成代码，从而在软件开发领域得到广泛应用。随着这一技术进步，研究人员和企业已开始将LLM集成到硬件设计与验证流程中。然而，这些功能强大的LLM也可能在硬件开发流程中利用安全漏洞引发新的攻击场景。其中尚未被深入探索的攻击途径是知识产权（IP）盗用。鉴于此类攻击可能表现为通过重写硬件设计以规避盗版检测，必须全面评估LLM执行此类任务的能力，并检验现有IP盗用检测工具的防御效能。为此，本研究提出LLMPirate——首个基于LLM的技术方案，能够生成可成功规避多种前沿盗版检测工具识别的电路设计盗版变体。我们针对LLM与硬件电路设计的集成挑战、大规模电路的可扩展性及攻击有效性提出三项解决方案，构建出端到端的自动化、高效且实用的技术框架。我们使用八个不同规模与能力的LLM对LLMPirate进行广泛实验评估，并测试其在盗用多种电路设计时对抗四种先进且广泛使用的盗版检测工具的表现。实验结果表明，LLMPirate在所有检测工具中均能实现100%测试电路的持续规避检测。此外，我们通过对IBEX与MOR1KX处理器及GPS模块的成功盗用案例研究，展示了LLMPirate的实际影响。我们期望本研究能推动更完善的IP盗用检测工具的研发。