Summary of Adversarial Attacks on Large Language Models in Medicine, by Yifan Yang et al.
Adversarial Attacks on Large Language Models in Medicine
by Yifan Yang, Qiao Jin, Furong Huang, Zhiyong Lu
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 integration of Large Language Models (LLMs) into healthcare applications offers promising advancements in medical diagnostics, treatment recommendations, and patient care. However, LLMs are susceptible to adversarial attacks, which poses a significant threat to delicate medical contexts. This study investigates the vulnerability of LLMs to two types of attacks in three medical tasks using real-world patient data. The results show that both open-source and proprietary LLMs are vulnerable across multiple tasks. Domain-specific tasks require more adversarial data for effective attack execution, especially for capable models. While integrating adversarial data does not degrade overall model performance on medical benchmarks, it leads to noticeable shifts in fine-tuned model weights, suggesting a potential pathway for detecting and countering attacks. This research highlights the need for robust security measures and defensive mechanisms to safeguard LLMs in medical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are being used in healthcare to help doctors make better decisions. But what if someone tried to trick these models into making bad choices? That’s exactly what this study looked at – how easy it is to trick LLMs in three different tasks, like diagnosing diseases or recommending treatments. The researchers found that even the best LLMs can be fooled, especially when they’re trying to make decisions about specific areas of medicine, like cancer treatment. This isn’t a reason to stop using LLMs, but rather a reminder that we need to make sure these models are safe and secure before we start relying on them in hospitals. |
