A Survey of Backdoor Attacks and Defenses on Large Language Models: Implications for Security Measures

arXiv:2406.06852v1 Announce Type: new
Abstract: The large language models (LLMs), which bridge the gap between human language understanding and complex problem-solving, achieve state-of-the-art performance on several NLP tasks, particularly in few-shot and zero-shot settings. Despite the demonstrable efficacy of LMMs, due to constraints on computational resources, users have to engage with open-source language models or outsource the entire training process to third-party platforms. However, research has demonstrated that language models are susceptible to potential security vulnerabilities, particularly in backdoor attacks. …

art arxiv attacks backdoor backdoor attacks bridge computational constraints cs.ai cs.cl cs.cr defenses gap human language language models large llms nlp performance problem problem-solving security security measures settings state survey understanding