Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak
  Attacks?
Various jailbreak attacks have been proposed to red-team Large Language
Models (LLMs) and revealed the vulnerable safeguards of LLMs. Besides, some
methods are not limited to the textual modality and extend the jailbreak attack
to Multimodal Large Language Models (MLLMs) by perturbing the visual input.
However, the absence of a universal evaluation benchmark complicates the
performance reproduction and fair comparison. Besides, there is a lack of
comprehensive evaluation of closed-source state-of-the-art (SOTA) models,
especially MLLMs, such as GPT-4V. To address these issues, this work first
builds a comprehensive jailbreak evaluation dataset with 1445 harmful questions
covering 11 different safety policies. Based on this dataset, extensive
red-teaming experiments are conducted on 11 different LLMs and MLLMs, including
both SOTA proprietary models and open-source models. We then conduct a deep
analysis of the evaluated results and find that (1) GPT4 and GPT-4V demonstrate
better robustness against jailbreak attacks compared to open-source LLMs and
MLLMs. (2) Llama2 and Qwen-VL-Chat are more robust compared to other
open-source models. (3) The transferability of visual jailbreak methods is
relatively limited compared to textual jailbreak methods. The dataset and code
can be found here
https://anonymous.4open.science/r/red_teaming_gpt4-C1CE/README.md .
660f6368adee377f87919472
Shuo Chen
claimed_verified
2024-09-05T15:31:33.682Z
648cbea3dee03837c823cbf2
/avatars/3f8c36436a5cbff2948df099ae604418.svg
Shuo Chen
user
ShuoChen99
660f6368adee377f87919473
Zhen Han
660f6368adee377f87919474
Bailan He
660f6368adee377f87919475
Zifeng Ding
claimed_verified
2024-05-03T15:14:36.007Z
662cdcebb10f82aa4f015941
/avatars/27381448b353b0463961a0d0b51f6ed3.svg
Zifeng Ding
user
Beanbagdzf
660f6368adee377f87919476
Wenqian Yu
660f6368adee377f87919477
Philip Torr
660f6368adee377f87919478
Volker Tresp
660f6368adee377f87919479
Jindong Gu
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak
  Attacks?
Various jailbreak attacks have been proposed to red-team Large Language
Models (LLMs) and revealed the vulnerable safeguards of LLMs. Besides, some
methods are not limited to the textual modality and extend the jailbreak attack
to Multimodal Large Language Models (MLLMs) by perturbing the visual input.
However, the absence of a universal evaluation benchmark complicates the
performance reproduction and fair comparison. Besides, there is a lack of
comprehensive evaluation of closed-source state-of-the-art (SOTA) models,
especially MLLMs, such as GPT-4V. To address these issues, this work first
builds a comprehensive jailbreak evaluation dataset with 1445 harmful questions
covering 11 different safety policies. Based on this dataset, extensive
red-teaming experiments are conducted on 11 different LLMs and MLLMs, including
both SOTA proprietary models and open-source models. We then conduct a deep
analysis of the evaluated results and find that (1) GPT4 and GPT-4V demonstrate
better robustness against jailbreak attacks compared to open-source LLMs and
MLLMs. (2) Llama2 and Qwen-VL-Chat are more robust compared to other
open-source models. (3) The transferability of visual jailbreak methods is
relatively limited compared to textual jailbreak methods. The dataset and code
can be found here
https://anonymous.4open.science/r/red_teaming_gpt4-C1CE/README.md .
A comprehensive jailbreak evaluation dataset is created to assess the robustness of various LLMs and MLLMs against jailbreak attacks, revealing differences in robustness between open-source and proprietary models, particularly in visual versus textual attacks.
Large Language Models Multimodal Large Language Models jailbreak attacks safety policies visual jailbreak textual jailbreak robustness transferability open-source models proprietary models