Welcome to the resource topic for 2025/1643
Title:
SCA-GPT: Generation-Plan-Tool Assisted LLM Agent for Full-Automated Side-Channel Analysis on Cryptosystems
Authors: Wenquan Zhou, An Wang, Yaoling Ding, Annv Liu, Jingqi Zhang, Jiakun Li, Liehuang Zhu
Abstract:Non-invasive security constitutes an essential component of hardware security, primarily involving side-channel analysis (SCA), with various international standards explicitly mandating rigorous testing. However, current SCA assessments heavily depend on expert manual procedures, resulting in significant expenditures of time and resources. Automated evaluation frameworks are not yet available. In recent years, Large Language Models (LLMs) have been widely adopted in various fields such as language generation, owing to their emergent capabilities. Particularly, LLM agents equipped with tool-usage capabilities have significantly expanded the potential of these models to interact with the physical world.
Motivated by these recent advances in LLM agents, we propose SCA-GPT, a LLM agent framework tailored for SCA tasks. The framework incorporates a Retrieval-Augmented Generation (RAG)-based expert knowledge base along with multiple SCA tools. We present a domain-specific expert knowledge base construction approach and two complementary evaluation metrics. Retrieval experiments validate the effectiveness of our knowledge base construction, achieving an average weighted score of 88.7% and an nDCG@5 of 90%, which demonstrates the contribution of structured expert entries to retrieval accuracy.By effectively infusing expert knowledge, SCA-GPT achieves fully automated, end-to-end ISO/IEC 17825-compliant tests. We conduct comprehensive experiments across three leading LLMs—DeepSeek V3, Kimi K2 and GLM 4.5—using datasets spanning seven cryptographic algorithms (e.g., AES, RSA, ECC, Kyber) and deploying on four hardware platforms, including smart cards, microcontrollers, and FPGAs. Results show that DeepSeek V3, Kimi K2, and GLM 4.5 achieve accuracies of 91.0%, 87.7%, and 82.0%, respectively, with the agent reducing testing time by an average of 76% compared with manual procedures. Notably, SCA-GPT is the first advanced LLM agent specifically designed for SCA tasks.
ePrint: https://eprint.iacr.org/2025/1643
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .