LLMPirate: LLMs for Black-box Hardware IP Piracy

📄 arXiv: 2411.16111v1 📥 PDF

作者: Vasudev Gohil, Matthew DeLorenzo, Veera Vishwa Achuta Sai Venkat Nallam, Joey See, Jeyavijayan Rajendran

分类: cs.CR, cs.AI

发布日期: 2024-11-25

备注: Accepted by NDSS Symposium 2025

💡 一句话要点

提出LLMPirate以解决硬件IP盗版检测问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 大型语言模型 硬件设计 知识产权 电路设计 盗版检测 安全性 自动化技术

📋 核心要点

  1. 现有的硬件IP盗版检测工具在面对LLM生成的电路设计变体时存在显著的检测漏洞。
  2. LLMPirate通过生成电路设计的盗版变体，成功规避了多种先进的盗版检测工具，展现了其创新性。
  3. 实验结果表明，LLMPirate在所有测试的电路上均能100%成功规避检测，显示出其高效性和实用性。

📝 摘要（中文）

随着大型语言模型（LLMs）的快速发展，它们在软件开发中的应用日益广泛。然而，这些强大的LLMs也可能引发新的安全漏洞，特别是在硬件设计和验证过程中。本文提出了LLMPirate，这是首个基于LLM的技术，能够生成成功规避多种先进盗版检测工具的电路设计盗版变体。我们设计了三种解决方案，以克服与LLM集成、扩展到大电路和有效性相关的挑战。通过对八种不同规模和能力的LLM进行广泛实验评估，LLMPirate在所有测试电路中均能100%成功规避检测，展示了其在实际应用中的潜力。

🔬 方法详解

问题定义：本文旨在解决硬件设计中的知识产权（IP）盗版问题，现有的检测工具在面对LLM生成的设计变体时效果不佳，导致安全隐患。

核心思路：LLMPirate利用LLM生成电路设计的盗版变体，设计上考虑了如何有效规避现有检测工具的能力，确保生成的变体在功能上与原设计相似但在结构上有所不同。

技术框架：LLMPirate的整体架构包括数据准备、LLM训练、变体生成和检测评估四个主要模块。首先，收集和准备电路设计数据，然后训练LLM以生成变体，最后通过多种检测工具评估生成的变体。

关键创新：LLMPirate的主要创新在于其能够生成多种电路设计的盗版变体，并且在所有测试中均能成功规避检测，这在现有研究中尚属首次。

关键设计：在设计过程中，LLMPirate采用了特定的参数设置和损失函数，以确保生成的变体在功能上保持一致，同时在结构上具有足够的差异性，以逃避检测。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，LLMPirate在所有测试的电路设计中均能100%成功规避四种先进的盗版检测工具，展现出其在实际应用中的强大能力和有效性。这一成果为硬件设计领域的安全性提供了新的视角。

🎯 应用场景

LLMPirate的研究成果在硬件设计和验证领域具有重要的应用潜力，特别是在保护知识产权和提升设计安全性方面。随着硬件设计的复杂性增加，LLMPirate能够帮助设计者理解和应对潜在的盗版风险，推动更安全的设计流程。

📄 摘要（原文）

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.