Adversarial Attacks on Large Language Models in Medicine
CoRR(2024)
Abstract
The integration of Large Language Models (LLMs) into healthcare applications
offers promising advancements in medical diagnostics, treatment
recommendations, and patient care. However, the susceptibility of LLMs to
adversarial attacks poses a significant threat, potentially leading to harmful
outcomes in delicate medical contexts. This study investigates the
vulnerability of LLMs to two types of adversarial attacks in three medical
tasks. Utilizing real-world patient data, we demonstrate that both open-source
and proprietary LLMs are susceptible to manipulation across multiple tasks.
This research further reveals that domain-specific tasks demand more
adversarial data in model fine-tuning than general domain tasks for effective
attack execution, especially for more capable models. We discover that while
integrating adversarial data does not markedly degrade overall model
performance on medical benchmarks, it does lead to noticeable shifts in
fine-tuned model weights, suggesting a potential pathway for detecting and
countering model attacks. This research highlights the urgent need for robust
security measures and the development of defensive mechanisms to safeguard LLMs
in medical applications, to ensure their safe and effective deployment in
healthcare settings.
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