Robustness Over Time: Understanding Adversarial Examples' Effectiveness on Longitudinal Versions of Large Language Models

arXiv:2308.07847v2 Announce Type: replace
Abstract: Large Language Models (LLMs) undergo continuous updates to improve user experience. However, prior research on the security and safety implications of LLMs has primarily focused on their specific versions, overlooking the impact of successive LLM updates. This prompts the need for a holistic understanding of the risks in these different versions of LLMs. To fill this gap, in this paper, we conduct a longitudinal study to examine the adversarial robustness -- specifically misclassification, jailbreak, and …

adversarial arxiv continuous cs.cr examples experience impact language language models large llm llms prompts research robustness safety security understanding updates user experience