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

arXiv: https://arxiv.org/abs/2504.07015 · HTML: https://mlsanity.com/paper/2504.07015/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2025.

## Built on by (most cited first, 5 of 5)

- [RTL++: Graph-enhanced LLM for RTL Code Generation](https://mlsanity.com/paper/2505.13479/) (2025)
- [DecoRTL: A Run-time Decoding Framework for RTL Code Generation with LLMs](https://mlsanity.com/paper/2507.02226/) (2025)
- [VerilogLAVD: LLM-Aided Rule Generation for Vulnerability Detection in Verilog](https://mlsanity.com/paper/2508.13092/) (2025)
- [SafeTune: Mitigating Data Poisoning in LLM Fine-Tuning for RTL Code Generation](https://mlsanity.com/paper/2604.27238/) (2026)
- [CASS-RTL: Correctness-Aware Subspace Steering for RTL Generation with LLMs](https://mlsanity.com/paper/2606.05680/) (2026)

## Abstract (arXiv)

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.