CAI Dataset is presented as the largest described corpus of LLM-driven hacker trajectories, with the claim that operator data concentration in frontier-model providers creates a major security risk best addressed by on-premise specialized LLMs.
Llama-3.1-foundationai-securityllm- base-8b technical report
10 Pith papers cite this work. Polarity classification is still indexing.
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ShellGames is an LLM-based SSH shell simulator that integrates five techniques to achieve 0.898 command accuracy, 0.918 sequence consistency, 0.98 state tracking, and 0.95 robustness on a new benchmark, plus comparable realism to real shells in a 20-person user study.
Introduces an evaluation framework for autonomous defense agents hardening commercial EDR, tested in a GOAD lab with Microsoft Defender XDR and two LLMs, revealing three lessons on telemetry design, per-policy attribution, and variable EDR behavior.
A hybrid LLM-RL red teaming framework generates adaptive attack campaigns in simulated enterprise networks to evaluate the robustness of AI-enabled SOAR systems.
Fine-tuning security LLMs specializes inherited classification circuits into token-level indicators that preserve canonical accuracy but fail under behavior-preserving transformations like aliasing and case mutation.
ACSE estimates LLM uncertainty via adaptive semantic entropy clustering with conformal prediction guarantees, reporting higher AUROC than token entropy baselines on datasets like TriviaQA.
KIT's IWSLT submission uses segment concatenation, LLM label generation and cross-lingual translation to create >1M long-form training instances and shows that likelihood re-ranking harms semantic tasks unless combined with Minimum Bayes Risk decoding.
CORE is a lightweight two-stage prompt compression method for edge-device RAG QA that builds answer and clue sets via NER and semantic matching then refines them to deliver higher accuracy and lower resource costs than baselines.
Domain-adapted LLMs and SLMs do not consistently outperform general models on STRIDE threat classification for 5G, with decoding strategies and model scale affecting validity but gains remaining insufficient for reliable use.
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.
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Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights
Domain-adapted LLMs and SLMs do not consistently outperform general models on STRIDE threat classification for 5G, with decoding strategies and model scale affecting validity but gains remaining insufficient for reliable use.