LLMPirate：利用大语言模型进行黑盒硬件知识产权盗版
LLMPirate: LLMs for Black-box Hardware IP Piracy
AI总结：
本文提出 LLMPirate，首个基于大语言模型的黑盒硬件 IP 盗版技术，通过三种方案实现端到端自动生成可逃避多种先进检测工具的电路盗版变体，并在多个处理器和 GPS 模块上验证了其有效性。
摘要：
大语言模型（LLMs）的快速发展使其能够近乎即时地有效分析和生成代码，从而在软件开发中得到广泛应用。继这一进展之后，研究人员和公司已开始将 LLMs 集成到硬件设计与验证流程中。然而，这些功能强大的 LLMs 也可能在硬件开发过程中针对安全漏洞引发新的攻击场景。其中一个尚未被探索的攻击向量是知识产权（IP）盗版。鉴于这种攻击可以表现为重写硬件设计以逃避盗版检测，因此必须全面评估 LLMs 执行该任务的能力，并评估当前 IP 盗版检测工具的缓解能力。因此，在本工作中，我们提出了 LLMPirate，这是第一种基于 LLM 的技术，能够生成电路设计的盗版变体，并成功逃避多个最先进的盗版检测工具的检测。我们设计了三种解决方案来克服与将 LLMs 集成到硬件电路设计、扩展到大型电路以及有效性相关的挑战，从而形成一种端到端的自动化、高效且实用的方法。我们使用八个不同规模和能力的 LLMs 对 LLMPirate 进行了广泛的实验评估，并评估了它们在盗版各种电路设计时对抗四个最先进且广泛使用的盗版检测工具的性能。我们的实验表明，LLMPirate 能够在所有检测工具上对 100% 的测试电路持续逃避检测。此外，我们通过对 IBEX 和 MOR1KX 处理器以及一个 GPS 模块的案例研究展示了 LLMPirate 的后果，我们成功对其进行了盗版。我们期望我们的工作能够激励并促进更好的 IP 盗版检测工具的开发。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
