TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning

TL;DR

TrojanForge is a reinforcement learning-based tool that automatically generates hardware Trojan examples capable of bypassing existing detectors, revealing vulnerabilities and aiding in the development of more robust defenses.

Contribution

This paper introduces TrojanForge, a novel RL environment and tool for generating adversarial hardware Trojan examples that outperform traditional methods.

Findings

High attack success rates against HT detectors

Reveals weaknesses in current HT detection methods

Provides insights for improving HT defense strategies

Abstract

The Hardware Trojan (HT) problem can be thought of as a continuous game between attackers and defenders, each striving to outsmart the other by leveraging any available means for an advantage. Machine Learning (ML) has recently played a key role in advancing HT research. Various novel techniques, such as Reinforcement Learning (RL) and Graph Neural Networks (GNNs), have shown HT insertion and detection capabilities. HT insertion with ML techniques, specifically, has seen a spike in research activity due to the shortcomings of conventional HT benchmarks and the inherent human design bias that occurs when we create them. This work continues this innovation by presenting a tool called TrojanForge, capable of generating HT adversarial examples that defeat HT detectors; demonstrating the capabilities of GAN-like adversarial tools for automatic HT insertion. We introduce an RL environment where the RL insertion agent interacts with HT detectors in an insertion-detection loop where the agent collects rewards based on its success in bypassing HT detectors. Our results show that this process helps inserted HTs evade various HT detectors, achieving high attack success percentages. This tool provides insight into why HT insertion fails in some instances and how we can leverage this knowledge in defense.