LLMPirate: LLMs for Black-box Hardware IP Piracy

Abstract: The rapid advancement of 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.

• The paper introduces LLMPirate, a novel technique that automates hardware IP piracy using LLMs through prompt syntax translation, netlist segmentation, and iterative feedback.
• It employs a divide-and-conquer approach with pre-characterization of netlists to overcome LLM limitations in handling Verilog syntax and large designs.
• Evaluation shows LLMPirate evades leading detection tools like GNN4IP, MOSS, Jplag, and SIM, exposing critical vulnerabilities in current hardware security measures.

LLMPirate: LLMs for Black-box Hardware IP Piracy

Introduction

The emergence of LLMs has profoundly impacted software and hardware design industries, providing advanced capabilities for generating code and automating diverse tasks. Yet, unaddressed security risks exist; LLMs can be misused to exploit vulnerabilities in the hardware development process, notably in intellectual property (IP) piracy. The paper "LLMPirate: LLMs for Black-box Hardware IP Piracy" introduces LLMPirate, a pioneering approach utilizing LLMs to create pirated hardware designs that evades detection tools successfully. This work formulates solutions to inherent LLM limitations in understanding hardware designs, resulting in an end-to-end automated framework for IP piracy.

Methodology and Solutions

LLMPirate addresses several challenges associated with deploying LLMs in hardware design piracy. Initially, LLMs struggle with Verilog's syntax and hardware component functionality. Furthermore, large netlists exceed the token context windows of LLMs. To combat these issues, LLMPirate proposes three primary solutions:

• Solution A: Prompt Syntax Translation: Transforming Verilog netlists into generic Boolean expressions enables LLMs to grasp and rewrite hardware circuits more effectively. This process extracts only relevant gates and converts their syntax, allowing LLMs to comprehend and generate structurally equivalent yet functionally consistent designs.
• Solution B: Pre-characterization and Divide-and-Conquer: By analyzing netlists to extract unique gate types, LLMPirate uses a divide-and-conquer approach. Each gate type is rewritten independently, overcoming the limited token context windows of LLMs and ensuring scalability to large designs.
• Solution C: Feedback-guided Interactive Formulation: LLMPirate incorporates an iterative process with multi-layered feedback to resolve error-prone responses. LLMs are provided multiple attempts with guided feedback on syntax, operator use, and equivalence, significantly improving response accuracy.

  Figure 1: High-level overview of our proposed technique.

Evaluation and Results

By testing LLMPirate across various LLMs and multiple piracy detection tools, this work demonstrates comprehensive evasion of state-of-the-art tools such as GNN4IP, MOSS, Jplag, and SIM. Results show consistent evasion rates with the random mapping strategy achieving superior performance due to its non-deterministic approach.

• Detection Tools: Evaluation against GNN4IP, MOSS, Jplag, and SIM highlights LLMPirate's ability to generate variants with lower similarity scores, thus evading detection. The random strategy outperformed deterministic approaches, further reducing detection rates.

  Figure 2: LLMPirate's end-to-end automated flow. All steps, including characterization, prompt syntax translation, syntax, operator and functionality checks, feedback, and the generation of pirated netlists using the LLM-generated transformations are end-to-end automated, and no manual intervention is needed.

• LLMs Performance: LLMs like GPT-4 and CoPilot demonstrate superior performance in pirating hardware IPs across detection tools. Interestingly, open-source models like Llama3 exhibit significant improvement through interactive feedback, showcasing the potential of smaller LLMs with guided support.

Implications and Future Work

The success of LLMPirate underscores the necessity to enhance existing piracy detection tools and suggests the development of robust countermeasures considering the novel evasion techniques presented. As evasion capabilities evolve, reinforcement of security protocols becomes critical in the hardware design workflow. Future research may explore integrating LLMPirate's techniques into existing frameworks or developing new detection paradigms that account for LLM-based piracy.

Figure 3: LLMPirate's best performance against GNN4IP~\cite{GNN4IP.

Conclusion

LLMPirate effectively illustrates the potential misuse of advanced LLMs in hardware IP piracy, identifying vulnerabilities in existing detection methodologies. By implementing innovative strategies to navigate LLM limitations in hardware design understanding, LLMPirate presents a formidable threat model that evades detection across multiple state-of-the-art tools. This work provokes essential dialogue in the research community to bolster hardware security against emerging threats, ensuring protection of valuable intellectual property amid rapid technological advancements.