Jason Xue

We propose a proactive defensive framework against malicious production LLM-based speech synthesis to protect our voice information.

This paper proposes a novel, training-free defense method for LVLMs that amplifies their inherent safety capabilities by identifying and utilizing a single safe attention head to detect unsafe inputs and guide safer responses.

We propose the first defense framework designed for Audio-Language Models to defend against jailbreak attacks.