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DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense

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arXiv:2606. 21059v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise for supporting decision-making in cybersecurity, but their reliability in high-stakes, time-evolving environments remains limited due to hallucinations, poor temporal reasoning, and shallow grounding in system context.

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Authors: Zhen Wang, Kristen Moore, Qin Wang, Guangsheng Yu, Minjune Kim, Diksha Goel, Gang Li, Ahmed Ibrahim, Ahmad Mohsin, Helge Janicke
Released: 2026-06-23 (16d ago)
Topic: Related to current project
Relation to my work: Read next because DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, latin, rect, correct, eval, assistant, line, rate. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.21059

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