TeleHunt is a modular framework that applies reference-driven snowballing with message-level classification and market labeling to discover Telegram cybercriminal communities, delivering the first systematic strategy comparison and a 172-million-message labeled dataset from 6,022 groups.
Large language models for cyber security: A systematic literature review
6 Pith papers cite this work, alongside 78 external citations. Polarity classification is still indexing.
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Under DC-only transfer attacks, LLM IDS vulnerability is substantial but dataset- and comparator-dependent, with gradient/score attacks transferring better than greedy ones.
DEFENGRAPH integrates a dual-layer static-dynamic KG with LLMs via path retrieval, filtering, and re-ranking, raising reasoning-recall from 61.45% to 73.49% and ticket-action recall from 52.17% to 72.46% on GPT-4o in live red-blue cyber range data.
ASTRAL applies multimodal LLMs with prompt chaining and few-shot learning to synthesize CPS architectures from disparate sources, enabling adaptive threat identification and quantitative risk estimation, as supported by ablation studies and feedback from 14 cybersecurity practitioners.
Empirical tests show open-source LLM agents underperform the Bandit SAST tool and are not ready to replace it for security scanning.
XekRung achieves state-of-the-art performance on cybersecurity benchmarks among same-scale models via tailored data synthesis and multi-stage training while retaining strong general capabilities.
citing papers explorer
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TeleHunt: A Framework and Tool for Efficient Cybercriminal Community Discovery on Telegram
TeleHunt is a modular framework that applies reference-driven snowballing with message-level classification and market labeling to discover Telegram cybercriminal communities, delivering the first systematic strategy comparison and a 172-million-message labeled dataset from 6,022 groups.
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Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers
Under DC-only transfer attacks, LLM IDS vulnerability is substantial but dataset- and comparator-dependent, with gradient/score attacks transferring better than greedy ones.
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DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense
DEFENGRAPH integrates a dual-layer static-dynamic KG with LLMs via path retrieval, filtering, and re-ranking, raising reasoning-recall from 61.45% to 73.49% and ticket-action recall from 52.17% to 72.46% on GPT-4o in live red-blue cyber range data.
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From Incomplete Architecture to Quantified Risk: Multimodal LLM-Driven Security Assessment for Cyber-Physical Systems
ASTRAL applies multimodal LLMs with prompt chaining and few-shot learning to synthesize CPS architectures from disparate sources, enabling adaptive threat identification and quantitative risk estimation, as supported by ablation studies and feedback from 14 cybersecurity practitioners.
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Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment
Empirical tests show open-source LLM agents underperform the Bandit SAST tool and are not ready to replace it for security scanning.
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XekRung Technical Report
XekRung achieves state-of-the-art performance on cybersecurity benchmarks among same-scale models via tailored data synthesis and multi-stage training while retaining strong general capabilities.