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Cybersecurity Threats and Mitigation Strategies for Large Language Models in Health Care.

Tugba Akinci D'Antonoli^1,2, Ali S Tejani³, Bardia Khosravi⁴

¹Department of Diagnostic and Interventional Neuroradiology, University Hospital Basel, Petersgraben 4, CH-4031, Basel, Switzerland.

Radiology. Artificial Intelligence

|May 14, 2025

Summary

Large language models (LLMs) in healthcare present unique cybersecurity risks, including data manipulation and privacy breaches. Implementing robust security measures is crucial for safe deployment and protecting patient information.

Keywords:

Application Domain Artificial Intelligence Computer Applications–General (Informatics)Cybersecurity Large Language Models

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Area of Science:

Medical Informatics
Cybersecurity
Artificial Intelligence

Background:

Large language models (LLMs) offer significant potential for advancing healthcare practices and patient outcomes.
However, their integration introduces unique cybersecurity vulnerabilities beyond those of typical AI systems.

Purpose of the Study:

To identify and analyze the specific cybersecurity challenges posed by LLMs in healthcare settings.
To propose mitigation strategies for addressing these risks and ensuring secure implementation.

Main Methods:

Review of potential LLM exploitation vectors in healthcare.
Analysis of threats including data breaches, unauthorized manipulation, and inference of sensitive information.
Examination of data poisoning techniques targeting LLMs.

Main Results:

LLMs are vulnerable to malicious attacks, privacy breaches, and unauthorized data manipulation.
Sensitive patient information can be inferred from LLM training data by malicious actors.
Data poisoning can alter LLM outputs to benefit attackers.

Conclusions:

Addressing LLM cybersecurity risks is essential before widespread healthcare adoption.
Implementing strong security measures during development, training, and deployment is key.
Proactive mitigation strategies are necessary to protect patient privacy and data integrity.