Transformers for Secure Hardware Systems: Applications, Challenges, and Outlook

TL;DR

This paper surveys the rising role of Transformer-based models in securing hardware systems against threats such as side-channel exploits, hardware Trojans, and firmware vulnerabilities. It synthesizes a wide range of applications, including HT detection, defect and counterfeit detection, malware detection, device fingerprinting, remote attestation, insider threat analysis, and information-flow tracking, highlighting how self-attention enables robust modeling of temporal and multi-modal hardware data. Key contributions include cataloging state-of-the-art Transformer adaptations (e.g., HTrans, TrojanFormer, EstraNet, TransNet, SHERLOCK, NtNDet, VulExplainer) and identifying practical challenges—computation, explainability, adversarial risk, and data availability—along with potential remedies such as efficient attention, edge offloading, and hybrid symbolic-ML approaches. The findings suggest that Transformer-based methods can form a foundation for next-generation, intelligent hardware defenses, with significant implications for real-time threat detection and secure hardware design, provided ongoing research addresses realism, robustness, and dataset access.

Abstract

The rise of hardware-level security threats, such as side-channel attacks, hardware Trojans, and firmware vulnerabilities, demands advanced detection mechanisms that are more intelligent and adaptive. Traditional methods often fall short in addressing the complexity and evasiveness of modern attacks, driving increased interest in machine learning-based solutions. Among these, Transformer models, widely recognized for their success in natural language processing and computer vision, have gained traction in the security domain due to their ability to model complex dependencies, offering enhanced capabilities in identifying vulnerabilities, detecting anomalies, and reinforcing system integrity. This survey provides a comprehensive review of recent advancements on the use of Transformers in hardware security, examining their application across key areas such as side-channel analysis, hardware Trojan detection, vulnerability classification, device fingerprinting, and firmware security. Furthermore, we discuss the practical challenges of applying Transformers to secure hardware systems, and highlight opportunities and future research directions that position them as a foundation for next-generation hardware-assisted security. These insights pave the way for deeper integration of AI-driven techniques into hardware security frameworks, enabling more resilient and intelligent defenses.

Figures (2)

• Figure 1: Scenarios where Transformers detect, classify, and mitigate vulnerabilities.
• Figure 2: Architecture of the Vanilla Transformer, featuring (1) an Encoder-Decoder structure, (2) Multi-Head Self-Attention for capturing diverse contextual relationships, and (3) Scaled Dot-Product Attention for efficient information weighting.