CyberCertBench shows frontier LLMs reach human-expert performance on general IT and networking security but drop on vendor-specific and formal standards questions such as IEC 62443, with a new framework for producing interpretable explanations.
arXiv preprint arXiv:2503.11917(2025)
5 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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AI agents enable adaptive computer worms that propagate autonomously by reasoning about targets and synthesizing attacks using LLMs on stolen compute.
A tiered server benchmark with 300 targets shows current LLMs achieve autonomous penetration success rates of 10.7-69.3% using only general cybersecurity tools and no target-specific knowledge.
A systematic review of neuro-symbolic AI in cybersecurity finds that deeper integration and causal reasoning improve performance across intrusion detection and vulnerability tasks, while identifying barriers and a research roadmap.
Gemini 2.5 Pro and Flash models are presented as achieving frontier performance in reasoning, coding, and long-context multimodal tasks while spanning a cost-capability Pareto curve.
citing papers explorer
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CyberCertBench: Evaluating LLMs in Cybersecurity Certification Knowledge
CyberCertBench shows frontier LLMs reach human-expert performance on general IT and networking security but drop on vendor-specific and formal standards questions such as IEC 62443, with a new framework for producing interpretable explanations.
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AI Agents Enable Adaptive Computer Worms
AI agents enable adaptive computer worms that propagate autonomously by reasoning about targets and synthesizing attacks using LLMs on stolen compute.
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The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems
A tiered server benchmark with 300 targets shows current LLMs achieve autonomous penetration success rates of 10.7-69.3% using only general cybersecurity tools and no target-specific knowledge.
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Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities
A systematic review of neuro-symbolic AI in cybersecurity finds that deeper integration and causal reasoning improve performance across intrusion detection and vulnerability tasks, while identifying barriers and a research roadmap.
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Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gemini 2.5 Pro and Flash models are presented as achieving frontier performance in reasoning, coding, and long-context multimodal tasks while spanning a cost-capability Pareto curve.