arXiv AI By Vita Santa Barletta, Danilo Caivano, Gabriel Cellammare, Andrea Dimauro, Pasquale Mesagna, Massimiliano Morga

A Digital Twin to Improve Cyber-Social Impact

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arXiv AI
Jun 12

The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems

arXiv:2606. 13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross.

By Jiaqi Luo, Jiarun Dai, Zhile Chen, Jia Xu, Weibing Wang, Yawen Duan, Brian Tse, Geng Hong, Xudong Pan, Yuan Zhang, Min Yang
arXiv AI
Oct 15

From Chasing Ghosts to Missed Attacks: Perspectives and Perceptions of SOC Practitioners on LLM Integration, Risks, and Readiness

The paper investigates how Security Operations Center (SOC) analysts perceive the integration of Large Language Models (LLMs) into their workflows. Through 25 interviews and interactive scenarios, the authors identify 15 LLM use cases across six functional categories, noting that while LLMs are useful for automating repetitive tasks, they are not yet suitable for high‑impact incident analysis due to limitations in technical depth, context awareness, and organization‑specific knowledge. Despite these concerns, practitioners show a strong willingness to adopt LLMs, driven by competitive pressure and a desire for operational efficiency.

By Jonas Thurner, Nadine Jost, Stefan Albert Horstmann, Fabian Ising, Lea Groeber, Alena Naiakshina, Sebastian Schinzel