LATENT: LLM-Augmented Trojan Insertion and Evaluation Framework for Analog Netlist Topologies

TL;DR

LATENT is a novel framework leveraging large language models to generate and evaluate stealthy analog Trojans in integrated circuits, enhancing understanding and detection of these security threats.

Contribution

This work introduces the first LLM-driven framework for creating and refining analog Trojans, addressing the lack of diverse and stealthy Trojan instances in security research.

Findings

Trojan activation range averages 15.74%, remaining inactive under most voltages.

Trojan insertion causes 11.3% performance degradation upon activation.

Framework effectively produces stealthy, circuit-specific Trojan designs.

Abstract

Analog and mixed-signal (A/MS) integrated circuits (ICs) are integral to safety-critical applications. However, the globalization and outsourcing of A/MS ICs to untrusted third-party foundries expose them to security threats, particularly analog Trojans. Unlike digital Trojans which have been extensively studied, analog Trojans remain largely unexplored. There has been only limited research on their diversity and stealth in analog designs, where a Trojan is activated only during a narrow input voltage range. Effective defense techniques require a clear understanding of the attack vectors; however, the lack of diverse analog Trojan instances limits robust advances in detection strategies. To address this gap, we present LATENT, the first large language model (LLM)-driven framework for crafting stealthy, circuit-specific analog Trojans. LATENT incorporates LLM as an autonomous agent to intelligently insert and refine Trojan components within analog designs based on iterative feedback from a detection model. This feedback loop ensures that the inserted Trojans remain stealthy while successfully evading detection. Experimental results demonstrate that our generated Trojan designs exhibit an average Trojan-activation range of 15.74%, ensuring they remain inactive under most operating voltages, while causing a significant performance degradation of 11.3% upon activation.