LLM-IFT: LLM-Powered Information Flow Tracking for Secure Hardware

• URL: http://arxiv.org/abs/2504.07015v1
• Date: Wed, 09 Apr 2025 16:32:13 GMT
• Title: LLM-IFT: LLM-Powered Information Flow Tracking for Secure Hardware
• Authors: Nowfel Mashnoor, Mohammad Akyash, Hadi Kamali, Kimia Azar,
• Abstract summary: Information flow tracking (IFT) is used to identify unauthorized activities that may compromise confidentiality or/and integrity in hardware.<n>Traditional IFT methods struggle with scalability and adaptability, leading to tracing bottlenecks that limit applicability in large-scale hardware.<n>This paper introduces LLM-IFT that integrates large language models (LLM) for the realization of the IFT process in hardware.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As modern hardware designs grow in complexity and size, ensuring security across the confidentiality, integrity, and availability (CIA) triad becomes increasingly challenging. Information flow tracking (IFT) is a widely-used approach to tracing data propagation, identifying unauthorized activities that may compromise confidentiality or/and integrity in hardware. However, traditional IFT methods struggle with scalability and adaptability, particularly in high-density and interconnected architectures, leading to tracing bottlenecks that limit applicability in large-scale hardware. To address these limitations and show the potential of transformer-based models in integrated circuit (IC) design, this paper introduces LLM-IFT that integrates large language models (LLM) for the realization of the IFT process in hardware. LLM-IFT exploits LLM-driven structured reasoning to perform hierarchical dependency analysis, systematically breaking down even the most complex designs. Through a multi-step LLM invocation, the framework analyzes both intra-module and inter-module dependencies, enabling comprehensive IFT assessment. By focusing on a set of Trust-Hub vulnerability test cases at both the IP level and the SoC level, our experiments demonstrate a 100\% success rate in accurate IFT analysis for confidentiality and integrity checks in hardware.

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