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

2025-04-09

AI Summary

Traditional information-flow tracking (IFT) methods face scalability and cross-module adaptability bottlenecks in complex hardware designs. To address this, this paper pioneers the integration of large language models (LLMs) into hardware security verification, proposing an automated IFT framework grounded in structured prompt engineering and hierarchical dependency modeling. The method enables fine-grained, multi-step reasoning across IP and SoC abstraction levels, comprehensively supporting verification of the confidentiality-integrity-availability (CIA) triad. Evaluated on the Trust-Hub benchmark suite, our approach achieves 100% IFT accuracy for both IP-level and SoC-level cases—demonstrating unprecedented scalability in high-density IC design contexts. This work establishes a novel, scalable paradigm for hardware security verification, overcoming longstanding limitations in automated information-flow analysis.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Ensuring security in complex modern hardware designs
• Scalability and adaptability issues in traditional IFT methods
• Comprehensive IFT assessment in high-density hardware architectures

Innovation

• LLM-driven hierarchical dependency analysis
• Multi-step LLM invocation for IFT
• Transformer-based models in IC design