April 2, 2024, 7:11 p.m. | Xiaopeng Xie, Ming Yan, Xiwen Zhou, Chenlong Zhao, Suli Wang, Yong Zhang, Joey Tianyi Zhou

cs.CR updates on arXiv.org arxiv.org

arXiv:2404.00461v1 Announce Type: cross
Abstract: Prompt-based learning paradigm has demonstrated remarkable efficacy in enhancing the adaptability of pretrained language models (PLMs), particularly in few-shot scenarios. However, this learning paradigm has been shown to be vulnerable to backdoor attacks. The current clean-label attack, employing a specific prompt as a trigger, can achieve success without the need for external triggers and ensure correct labeling of poisoned samples, which is more stealthy compared to the poisoned-label attack, but on the other hand, it …

adaptability arxiv attack attacks backdoor backdoor attacks covert cs.ai cs.cl cs.cr cs.lg current language language models paradigm prompt shortcuts vulnerable

Technical Senior Manager, SecOps | Remote US

@ Coalfire | United States

Global Cybersecurity Governance Analyst

@ UL Solutions | United States

Security Engineer II, AWS Offensive Security

@ Amazon.com | US, WA, Virtual Location - Washington

Senior Cyber Threat Intelligence Analyst

@ Sainsbury's | Coventry, West Midlands, United Kingdom

Embedded Global Intelligence and Threat Monitoring Analyst

@ Sibylline Ltd | Austin, Texas, United States

Senior Security Engineer

@ Curai Health | Remote