Integrated Speech and Large Language Models (SLMs) that can follow speech
instructions and generate relevant text responses have gained popularity
lately. However, the safety and robustness of these models remains largely
unclear. In this work, we investigate the potential vulnerabilities of such
instruction-following speech-language models to adversarial attacks and
jailbreaking. Specifically, we design algorithms that can generate adversarial
examples to jailbreak SLMs in both white-box and black-box attack settings
without human involvement. Additionally, we propose countermeasures to thwart
such jailbreaking attacks. Our models, trained on dialog data with speech
instructions, achieve state-of-the-art performance on spoken question-answering
task, scoring over 80% on both safety and helpfulness metrics. Despite safety
guardrails, experiments on jailbreaking demonstrate the vulnerability of SLMs
to adversarial perturbations and transfer attacks, with average attack success
rates of 90% and 10% respectively when evaluated on a dataset of carefully
designed harmful questions spanning 12 different toxic categories. However, we
demonstrate that our proposed countermeasures reduce the attack success
significantly.

通过设计算法进行白盒和黑盒攻击以实现越狱，提出了对抗越狱攻击的对策，并展示了该对策显著降低攻击成功率。