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Domain-adapted LLMs show mixed results for 5G threat modeling

By PulseAugur Editorial · [1 sources] ·

Researchers evaluated domain-adapted language models for threat modeling in 5G security using the STRIDE approach. Their empirical study, involving 52 configurations across 8 language models, found that domain adaptation did not consistently improve performance over general-purpose models. Decoding strategies and model scale showed significant impact, but larger models did not guarantee reliable threat modeling, suggesting a need for better task-specific reasoning and security grounding. AI

IMPACT Highlights limitations of current LLMs for structured threat modeling, suggesting a need for improved security reasoning.

RANK_REASON Academic paper evaluating LLMs for a specific cybersecurity task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Domain-adapted LLMs show mixed results for 5G threat modeling

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Academic paper evaluating LLMs for a specific cybersecurity task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ashraf Matrawy ·

    Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights

    Large Language Models(LLMs) are increasingly explored for cybersecurity applications such as vulnerability detection. In the domain of threat modelling, prior work has primarily evaluated a number of general-purpose Large Language Models under limited prompting settings. In this …

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