# SCAR: Power Side-Channel Analysis at RTL-Level

arXiv: https://arxiv.org/abs/2310.06257 · HTML: https://mlsanity.com/paper/2310.06257/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2023.

## Built on by (most cited first, 3 of 3)

- [LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust](https://mlsanity.com/paper/2405.07061/) (2024)
- [Enhancing Large Language Models for Hardware Verification: A Novel SystemVerilog Assertion Dataset](https://mlsanity.com/paper/2503.08923/) (2025)
- [Beyond TVLA: Anderson-Darling Leakage Assessment for Neural Network Side-Channel Leakage Detection](https://mlsanity.com/paper/2603.18647/) (2026)

## Abstract (arXiv)

Power side-channel attacks exploit the dynamic power consumption of cryptographic operations to leak sensitive information of encryption hardware. Therefore, it is necessary to conduct power side-channel analysis for assessing the susceptibility of cryptographic systems and mitigating potential risks. Existing power side-channel analysis primarily focuses on post-silicon implementations, which are inflexible in addressing design flaws, leading to costly and time-consuming post-fabrication design re-spins. Hence, pre-silicon power side-channel analysis is required for early detection of vulnerabilities to improve design robustness. In this paper, we introduce SCAR, a novel pre-silicon power side-channel analysis framework based on Graph Neural Networks (GNN). SCAR converts register-transfer level (RTL) designs of encryption hardware into control-data flow graphs and use that to detect the design modules susceptible to side-channel leakage. Furthermore, we incorporate a deep learning-based explainer in SCAR to generate quantifiable and human-accessible explanation of our detection and localization decisions. We have also developed a fortification component as a part of SCAR that uses large-language models (LLM) to automatically generate and insert additional design code at the localized zone to shore up the side-channel leakage. When evaluated on popular encryption algorithms like AES, RSA, and PRESENT, and postquantum cryptography algorithms like Saber and CRYSTALS-Kyber, SCAR, achieves up to 94.49% localization accuracy, 100% precision, and 90.48% recall. Additionally, through explainability analysis, SCAR reduces features for GNN model training by 57% while maintaining comparable accuracy. We believe that SCAR will transform the security-critical hardware design cycle, resulting in faster design closure at a reduced design cost.