SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large
  Language Models
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.
6644f96cbb4e1cdd58cf5589
Raghuveer Peri
claimed_verified
2024-05-16T12:33:34.909Z
6645eafac2e5c9986a694cc8
/avatars/bf3defa8a9cf5cbcd2a71386deb142eb.svg
Raghuveer Peri
user
rperi12
6644f96cbb4e1cdd58cf558a
Sai Muralidhar Jayanthi
admin_assigned
2024-05-15T20:21:07.538Z
5faf4c4984389b139cf3b4e0
/avatars/0fa06885e2999fcab8bc833e2211779f.svg
Sai Muralidhar Jayanthi
user
murali1996
6644f96cbb4e1cdd58cf558b
Srikanth Ronanki
claimed_verified
2024-05-17T06:53:30.174Z
66467366a6d12d11aa1907b7
/avatars/58e8cfc785e94ea415575408a2e78f24.svg
Srikanth Ronanki
user
ronanki123
6644f96cbb4e1cdd58cf558c
Anshu Bhatia
6644f96cbb4e1cdd58cf558d
Karel Mundnich
admin_assigned
2024-05-15T20:21:36.712Z
65b03e4dbf7d3327d066e7af
/avatars/ad60d2f207e80f742e5aaed93281b8a8.svg
Karel Mundnich
user
kmundnic
6644f96cbb4e1cdd58cf558e
Saket Dingliwal
admin_assigned
2024-05-15T20:21:54.140Z
60de4246264e80d074e52cd8
/avatars/cee9c01a2456c5266cada342a98a7e03.svg
Dingliwal
user
Saket
6644f96cbb4e1cdd58cf558f
Nilaksh Das
6644f96cbb4e1cdd58cf5590
Zejiang Hou
6644f96cbb4e1cdd58cf5591
Goeric Huybrechts
6644f96cbb4e1cdd58cf5592
Srikanth Vishnubhotla
6644f96cbb4e1cdd58cf5593
Daniel Garcia-Romero
admin_assigned
2024-05-15T20:22:34.461Z
63af027b5833626d76714bcd
/avatars/ab8f4266f9efd5c257597738d6ddcc6d.svg
Daniel Garcia-Romero
user
dgromero
6644f96cbb4e1cdd58cf5594
Sundararajan Srinivasan
6644f96cbb4e1cdd58cf5595
Kyu J Han
6644f96cbb4e1cdd58cf5596
Katrin Kirchhoff
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large
  Language Models
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.
Research investigates adversarial vulnerabilities and proposes countermeasures for speech-language models that follow speech instructions and generate text responses.
adversarial attacks jailbreaking instruction-following speech-language models white-box attacks black-box attacks countermeasures spoken question-answering task safety metrics helpfulness metrics harmful questions toxic categories transfer attacks