Cracking the Chip: AI-Powered Security for Semiconductor Threats

Abstract

Semiconductor supply chains have become increasingly vulnerable to sophisticated, low-level threats that originate in the early phases and propagate undetected across various stages of semiconductor device production. As semiconductor systems grow more complex and globally interconnected, these low-level design threats present significant risks, including data breaches, system failures, and long-term erosion of reliability. This paper presents a comprehensive AI-driven framework to detect, model, and mitigate hardware security threats across the semiconductor supply chain, from design and fabrication to assembly. We begin with the design phase, illustrating how vulnerabilities like hardware Trojans in third-party IP blocks, compromised EDA scripts, and speculative execution side-channels can be exploited. AI techniques, such as anomaly detection for logic integrity, dynamic hashing for secure script flows, and entropy-based instruction shuffling, are shown to proactively block or obfuscate these attacks. These models serve as templates for following stages, including fabrication (tampered masks or altered process flows), assembly and packaging (hardware fingerprinting), and post-silicon validation (malicious test routines or data exfiltration). Our contributions include a stage-wise breakdown of threat surfaces across the supply chain and the design of threat models with corresponding AI-driven defenses that analyze patterns, enforce trust boundaries, and obfuscate system behavior. Additionally, to assess the viability of these defenses, we outline a validation framework involving simulated and prototyped defenses, which include instruction shuffling, JTAG interface monitoring, and machine learning-based fault pattern analysis. Proposed evaluation metrics include detection accuracy, computational overhead, entropy of runtime traces, and classification accuracy. By addressing persistent security threats early and continuously through the chip lifecycle, we aim to leverage AI to shift hardware security from reactive patching to proactive risk management. Our work emphasizes the importance of securing semiconductor systems at their root, offering a path toward proactive hardware security and highlights the need for scalable, interdisciplinary solutions at the intersection of AI, hardware design, and supply chain security.