Automated Hardware Logic Obfuscation Framework Using GPT

TL;DR

This paper presents Obfus-chat, an innovative GPT-based framework that automates hardware obfuscation to protect intellectual property from reverse engineering, piracy, and tampering, demonstrating effective security enhancements.

Contribution

The paper introduces a novel GPT-driven framework for hardware obfuscation that automates design protection, a significant advancement over manual or traditional methods.

Findings

Effective obfuscation of hardware netlists against SAT attacks

Maintains functional correctness post-obfuscation

Demonstrates efficiency and security improvements using Trust-Hub benchmarks

Abstract

Obfuscation stands as a promising solution for safeguarding hardware intellectual property (IP) against a spectrum of threats including reverse engineering, IP piracy, and tampering. In this paper, we introduce Obfus-chat, a novel framework leveraging Generative Pre-trained Transformer (GPT) models to automate the obfuscation process. The proposed framework accepts hardware design netlists and key sizes as inputs, and autonomously generates obfuscated code tailored to enhance security. To evaluate the effectiveness of our approach, we employ the Trust-Hub Obfuscation Benchmark for comparative analysis. We employed SAT attacks to assess the security of the design, along with functional verification procedures to ensure that the obfuscated design remains consistent with the original. Our results demonstrate the efficacy and efficiency of the proposed framework in fortifying hardware IP against potential threats, thus providing a valuable contribution to the field of hardware security.