arXiv:2607. 09954v1 Announce Type: cross Abstract: Hardening IT on-premises environments can be a daunting task for teams without access to adequate cybersecurity expertise.
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The paper presents a gamified 20Q-style recommender for cybersecurity education that uses reinforcement learning and explainable AI to guide learners through interactive questioning. By acting as a knowledgeable questioner, the system narrows down user-described security scenarios, identifies the underlying threat, and transparently explains its reasoning. The authors detail the system architecture, algorithmic foundations, and provide case studies covering attack vectors such as the Cyber Kill Chain, phishing, ransomware, and web application vulnerabilities.
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.
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.
arXiv:2606. 02644v1 Announce Type: cross Abstract: Agentic scaffolds have dramatically improved LLM performance on complex, long-horizon tasks, yielding both broad benefits and amplified risks in domains like cybersecurity.
arXiv:2606. 28666v1 Announce Type: cross Abstract: Agent-based AI has enabled the automation of tasks by exposing application tools and resources to large language models (LLMs).
arXiv:2606. 28450v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly being integrated into real-world systems.