MiST (Mid-trained Security Transformer) is a suite of 8B and 32B language models tailored for cybersecurity, achieving strong performance on public benchmarks. The approach uses a mid-training stage that adapts general pre-trained models to the domain by curating a compact, expert-vetted seed corpus and generating high-quality synthetic training data, rather than continual pre-training on large raw text. MiST checkpoints improve mean cybersecurity accuracy by +13.1 and +8.6 absolute percentage points over Qwen baselines for 8B and 32B models, respectively, and provide a stronger initialization for downstream task-specific fine-tuning and reinforcement learning.
By Oded Ovadia, Elad Ben Zaken, Elad Guttman, Orly Moreno Kadosh
arXiv:2512. 05518v2 Announce Type: replace-cross Abstract: Open-source Large Language Models (LLMs) play a critical role in the democratization of AI, yet their "open" nature introduces more avenues for malicious actors to misuse them for harmful purposes.
By Jason Vega, Gagandeep Singh
arXiv:2607. 05001v1 Announce Type: cross Abstract: Cyber Threat Intelligence (CTI) reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated analysis and reasoning.
By Mouhamed Amine Bouchiha, Gregory Blanc
arXiv:2606. 18166v1 Announce Type: cross Abstract: Classifying Cyber Threat Intelligence (CTI) using MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) is essential for proactive defense, but historically required extensive human effort.
By Ahmed Ryan, Saad Sakib Noor, Md Erfan, Shaswata Mitra, Sudip Mittal, Md Rayhanur Rahman
arXiv:2606. 15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.
By Wenbo Yu, Bohua Wang, Hao Fang, Kuofeng Gao, Jingru Zeng, Xiaochen Yang, Tianyi Zhang, Xiaoxiao Ma, Jiawei Kong, Hao Wu, Bin Chen, Shu-Tao Xia, Min Zhang
arXiv:2512. 10244v2 Announce Type: replace-cross Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-specific examples to annotate the unlabeled ones.
By Tian Liu, Anwesha Basu, James Caverlee, Shu Kong
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:2606. 00686v1 Announce Type: new Abstract: The prevailing paradigm in large language model (LLM) alignment operates via erasure, filtering unsafe data or training models to strictly refuse harmful prompts.
By Maryam Hashemzadeh, Jerry Huang, Minseon Kim, Marc-Alexandre C\^ot\'e, Sarath Chandar
arXiv:2605. 18879v3 Announce Type: replace-cross Abstract: Large language models inevitably retain sensitive information, defined as inputs that may induce harmful generations, due to training on massive web corpora, raising concerns for privacy and safety.
By Yujie Lin, Chengyi Yang, Zhishang Xiang, Yiping Song, Jinsong Su
The paper introduces DisCTI, a system that automatically maps cyber threat intelligence (CTI) events to relevant industry sectors using a multilabel classification approach. By creating a dataset of 872 sector‑labelled CTI events and applying a BERT transformer model, the authors achieve a macro‑averaged F1‑score of 0.89, correctly assigning 94.5% of sector labels. This demonstrates that embedding expert knowledge into machine learning can enable timely, sector‑aware CTI dissemination, improving defensive response.
By Fajar Wijitrisnanto (National Cyber and Crypto Agency, Jakarta, Indonesia), Alsharif Abuadbba (CSIRO, Sydney, Australia), Yansong Gao (CSIRO, Sydney, Australia, The University of Western Australia, Perth, Australia), Nan Wu (CSIRO, Sydney, Australia)
arXiv:2607. 28862v1 Announce Type: cross Abstract: The rapid development of Large Language Models (LLMs) has led to significant advances across a wide range of language tasks, while simultaneously raising growing concerns about unauthorized data exploitation and privacy leakage.
By Chengshuai Zhao, Pingchuan Ma, Dawei Li, Bohan Jiang, Zhiyuan Yu, Zhen Tan, Huan Liu
arXiv:2606. 29069v1 Announce Type: new Abstract: Concept-based Explainable AI (C-XAI) seeks human-understandable explanations grounded in semantic concepts, yet validation is limited by the scarcity of fine-grained concept annotations.
By Darian Fern\'andez-Guti\'errez, Rafael Bello, Marilyn Bello, Natalia D\'iaz-Rodr\'iguez