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arXiv · 2606.21349

LLM-assisted Generation of Pseudo-C2 Servers for IoT Malware Dynamic Analysis

K. Hasui·S. Matsugaya·M. Shimamura·M. Hashimoto
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Abstract

Most IoT malware operates as botnets dependent on Command and Control (C2) servers, but the short-lived nature of attack infrastructure often leaves samples dormant without C2 communication, hindering dynamic analysis. This paper proposes a system that combines Ghidra with a Large Language Model (LLM) to extract communication specifications from a malware binary and automatically generate a pseudo-C2 server. Experiments using Mirai demonstrate that the proposed system semantically interprets binary control structures and extracts all 20 core protocol elements in agreement with the ground truth (100\% specification extraction accuracy). The generated pseudo-C2 server fully reproduces seven of ten DDoS attack vectors with attack behavior consistent with the original C2. When applied to a customized variant created by modifying the publicly available Mirai source code, the method succeeds end-to-end -- from specification extraction through pseudo-C2 generation to attack reproduction -- demonstrating that the LLM infers specifications from binary structures without relying on pre-trained knowledge. This approach extends the applicability of LLMs from analysis assistance to the automated construction of dynamic analysis environments.

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K. Hasui, S. Matsugaya, M. Shimamura, M. Hashimoto. 2026-06-19. LLM-assisted Generation of Pseudo-C2 Servers for IoT Malware Dynamic Analysis. https://doi.org/10.29007/7hdm

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