arXiv:2609.24710v1 Announce Type: cross
Abstract: Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent reasonin...
By Jiling Zhou, Aisvarya Adeseye, Antti Hakkala, Seppo Virtanen, Jouni Isoaho
arXiv:2606. 08649v1 Announce Type: cross Abstract: Forensic analysis of web server logs demands both accurate detection and human-readable explanations that can satisfy legal requirements.
By Bernhard Kneip, Nhien-An Le-Khac, Hong-Hanh Nguyen-Le
arXiv:2510. 14113v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domain-specific models and training datasets.
By Matan Levi, Daniel Ohayon, Ariel Blobstein, Ravid Sagi, Ian Molloy, Yair Allouche
arXiv:2607. 19742v1 Announce Type: cross Abstract: Cyber Threat Intelligence (CTI) reports richly describe real-world attack processes, but their unstructured narratives cannot be directly used for automated attack-path reasoning.
By Wenbo Hou, Ning Hu, Xueping Wang, Jiahao Gu, Wenjian Luo
The paper investigates whether large language models (LLMs) can perform structured security reasoning in cybersecurity decision-making. By testing LLMs on defense selection over attack graphs from real-world threat scenarios, the study finds that LLMs can produce coherent strategies when the attack-graph structure is explicitly provided, yet their performance is fragile, highly sensitive to prompt framing, and deteriorates with increasing graph complexity. Additionally, LLM-generated solvers recover the correct high-level formulation but scale poorly compared to specialized solvers.
By Pasquale Malacaria, Yunxiao Zhang
arXiv:2607. 20216v1 Announce Type: cross Abstract: Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours.
By Adel ElZemity, Shujun Li, Budi Arief