Summary of Cve-llm : Automatic Vulnerability Evaluation in Medical Device Industry Using Large Language Models, by Rikhiya Ghosh et al.
CVE-LLM : Automatic vulnerability evaluation in medical device industry using large language models
by Rikhiya Ghosh, Oladimeji Farri, Hans-Martin von Stockhausen, Martin Schmitt, George Marica Vasile
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solution leverages Large Language Models (LLMs) to automate vulnerability assessments for medical devices, enabling rapid mitigation efforts amidst an unprecedented wave of cybersecurity attacks impacting millions. The approach learns from historical evaluations and considers device characteristics, including existing security posture and controls. This medium-difficulty summary highlights the paper’s contributions: best practices for training a vulnerability LLM in an industrial context, a comprehensive comparison of Language Models’ effectiveness, and a new human-in-the-loop framework to expedite evaluation processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses AI to help keep medical devices safe from cyber attacks. They’re like robots that can read and understand lots of information, which makes them great at finding problems in devices before they cause harm. The researchers show how these AI models can be trained to quickly identify vulnerabilities and then work with humans to fix the problems. |
