mlsanityData to 7 Oct 2026

CVE-LLM : Automatic vulnerability evaluation in medical device industry using large language models

arXiv 2407.14640 ↗First posted 19 Jul 2024cs.CL cs.AI cs.CRcited by 0

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Abstract (arXiv)

The healthcare industry is currently experiencing an unprecedented wave of cybersecurity attacks, impacting millions of individuals. With the discovery of thousands of vulnerabilities each month, there is a pressing need to drive the automation of vulnerability assessment processes for medical devices, facilitating rapid mitigation efforts. Generative AI systems have revolutionized various industries, offering unparalleled opportunities for automation and increased efficiency. This paper presents a solution leveraging Large Language Models (LLMs) to learn from historical evaluations of vulnerabilities for the automatic assessment of vulnerabilities in the medical devices industry. This approach is applied within the portfolio of a single manufacturer, taking into account device characteristics, including existing security posture and controls. The primary contributions of this paper are threefold. Firstly, it provides a detailed examination of the best practices for training a vulnerability Language Model (LM) in an industrial context. Secondly, it presents a comprehensive comparison and insightful analysis of the effectiveness of Language Models in vulnerability assessment. Finally, it proposes a new human-in-the-loop framework to expedite vulnerability evaluation processes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 69 references that are in the map. First posted on arXiv 19 Jul 2024.

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References · 15

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  3. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  4. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,085
  5. 2023RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedbackcited by 615
  6. 2023Large Language Models in Finance: A Surveycited by 223
  7. 2023LLM-powered Data Augmentation for Enhanced Cross-lingual Performancecited by 51
  8. 2019An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Modelscited by 39
  9. 2023Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphscited by 35
  10. 2024Constrained Decoding for Secure Code Generationcited by 35
  11. 2024Multitask-based Evaluation of Open-Source LLM on Software Vulnerabilitycited by 23
  12. 2024LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Rewardcited by 16
3 more
  1. 2021CVSS-BERT: Explainable Natural Language Processing to Determine the Severity of a Computer Security Vulnerability from its Descriptioncited by 10
  2. 2024PLLM-CS: Pre-trained Large Language Model (LLM) for Cyber Threat Detection in Satellite Networkscited by 10
  3. 2021Linking Common Vulnerabilities and Exposures to the MITRE ATT&CK Framework: A Self-Distillation Approachcited by 9

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