Paper 2025/1643 Original: eprint.iacr.org/2025/1643 ↗

SCA-GPT: A Generation-Planning-Tool Assisted LLM Agent for Fully Automated Side-Channel Analysis on Cryptosystems

Abstract

Non-invasive security testing under standards such as ISO/IEC 17825 still relies on human experts: how to measure a given target, and what the standard requires once the results are available, differ with every device and algorithm class, so the procedure is composed anew for each assessment. We present SCA-GPT, a large language model (LLM) agent for side-channel evaluation: given only a single natural-language instruction, it completes a full standard-conformant assessment covering timing analysis, simple power analysis, and differential power analysis, reporting for every test item a verdict, the governing clause, and checkable numeric evidence. The agent integrates a domain-specific expert knowledge base curated from 155 documents with a suite of specialized side-channel analysis tools. In retrieval experiments, the expert knowledge base achieves a Hit Rate@5 of 89.7% and an MRR@5 of 74.1%. We further evaluate the full framework with three LLMs on 13 real datasets covering six cryptographic algorithms on smart-card, microcontroller, and FPGA targets. DeepSeek-V3.2, Kimi-K2.6, and Qwen3.5 achieve success rates of 90.0%, 87.7%, and 94.6%, respectively, versus 0.0% for a plain ReAct agent, at an average of 253 seconds per assessment. To our knowledge, SCA-GPT is the first automated LLM agent for standard-conformant side-channel evaluation.