@inproceedings{peri-etal-2024-speechguard,
title = "{S}peech{G}uard: Exploring the Adversarial Robustness of Multi-modal Large Language Models",
author = "Peri, Raghuveer and
Jayanthi, Sai Muralidhar and
Ronanki, Srikanth and
Bhatia, Anshu and
Mundnich, Karel and
Dingliwal, Saket and
Das, Nilaksh and
Hou, Zejiang and
Huybrechts, Goeric and
Vishnubhotla, Srikanth and
Garcia-Romero, Daniel and
Srinivasan, Sundararajan and
Han, Kyu and
Kirchhoff, Katrin",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.596",
pages = "10018--10035",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T SpeechGuard: Exploring the Adversarial Robustness of Multi-modal Large Language Models
%A Peri, Raghuveer
%A Jayanthi, Sai Muralidhar
%A Ronanki, Srikanth
%A Bhatia, Anshu
%A Mundnich, Karel
%A Dingliwal, Saket
%A Das, Nilaksh
%A Hou, Zejiang
%A Huybrechts, Goeric
%A Vishnubhotla, Srikanth
%A Garcia-Romero, Daniel
%A Srinivasan, Sundararajan
%A Han, Kyu
%A Kirchhoff, Katrin
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F peri-etal-2024-speechguard
%X 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.
%U https://aclanthology.org/2024.findings-acl.596
%P 10018-10035
Markdown (Informal)
[SpeechGuard: Exploring the Adversarial Robustness of Multi-modal Large Language Models](https://aclanthology.org/2024.findings-acl.596) (Peri et al., Findings 2024)
ACL
- Raghuveer Peri, Sai Muralidhar Jayanthi, Srikanth Ronanki, Anshu Bhatia, Karel Mundnich, Saket Dingliwal, Nilaksh Das, Zejiang Hou, Goeric Huybrechts, Srikanth Vishnubhotla, Daniel Garcia-Romero, Sundararajan Srinivasan, Kyu Han, and Katrin Kirchhoff. 2024. SpeechGuard: Exploring the Adversarial Robustness of Multi-modal Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 10018–10035, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.