Protecting Your LLMs with Information Bottleneck

arXiv:2404.13968v1 Announce Type: cross
Abstract: The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content. Despite efforts to ethically align LLMs, these are often fragile and can be circumvented by jailbreaking attacks through optimized or manual adversarial prompts. To address this, we introduce the Information Bottleneck Protector (IBProtector), a defense mechanism grounded in the information bottleneck principle, and we modify the objective to avoid trivial solutions. …

address adversarial arxiv attacks can cs.ai cs.cl cs.cr information jailbreaking language language models large llms natural natural language natural language processing prompts protecting