From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking
- URL: http://arxiv.org/abs/2406.14859v1
- Date: Fri, 21 Jun 2024 04:33:48 GMT
- Title: From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking
- Authors: Siyuan Wang, Zhuohan Long, Zhihao Fan, Zhongyu Wei,
- Abstract summary: Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have exposed vulnerabilities to various adversarial attacks.
This paper provides a comprehensive overview of jailbreaking research targeting both LLMs and MLLMs, highlighting recent advancements in evaluation benchmarks, attack techniques and defense strategies.
- Score: 32.300594239333236
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid development of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has exposed vulnerabilities to various adversarial attacks. This paper provides a comprehensive overview of jailbreaking research targeting both LLMs and MLLMs, highlighting recent advancements in evaluation benchmarks, attack techniques and defense strategies. Compared to the more advanced state of unimodal jailbreaking, multimodal domain remains underexplored. We summarize the limitations and potential research directions of multimodal jailbreaking, aiming to inspire future research and further enhance the robustness and security of MLLMs.
Related papers
- LLaVA-KD: A Framework of Distilling Multimodal Large Language Models [70.19607283302712]
We propose a novel framework to transfer knowledge from l-MLLM to s-MLLM.
Specifically, we introduce Multimodal Distillation (MDist) to minimize the divergence between the visual-textual output distributions of l-MLLM and s-MLLM.
We also propose a three-stage training scheme to fully exploit the potential of s-MLLM.
arXiv Detail & Related papers (2024-10-21T17:41:28Z) - $\textit{MMJ-Bench}$: A Comprehensive Study on Jailbreak Attacks and Defenses for Multimodal Large Language Models [11.02754617539271]
We introduce textitMMJ-Bench, a unified pipeline for evaluating jailbreak attacks and defense techniques for MLLMs.
We assess the effectiveness of various attack methods against SoTA MLLMs and evaluate the impact of defense mechanisms on both defense effectiveness and model utility.
arXiv Detail & Related papers (2024-08-16T00:18:23Z) - A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems.
This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
arXiv Detail & Related papers (2024-08-02T15:14:53Z) - A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends [78.3201480023907]
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks.
The vulnerability of LVLMs is relatively underexplored, posing potential security risks in daily usage.
In this paper, we provide a comprehensive review of the various forms of existing LVLM attacks.
arXiv Detail & Related papers (2024-07-10T06:57:58Z) - Benchmarking Trustworthiness of Multimodal Large Language Models: A Comprehensive Study [51.19622266249408]
MultiTrust is the first comprehensive and unified benchmark on the trustworthiness of MLLMs.
Our benchmark employs a rigorous evaluation strategy that addresses both multimodal risks and cross-modal impacts.
Extensive experiments with 21 modern MLLMs reveal some previously unexplored trustworthiness issues and risks.
arXiv Detail & Related papers (2024-06-11T08:38:13Z) - Efficient Multimodal Large Language Models: A Survey [60.7614299984182]
Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.
The extensive model size and high training and inference costs have hindered the widespread application of MLLMs in academia and industry.
This survey provides a comprehensive and systematic review of the current state of efficient MLLMs.
arXiv Detail & Related papers (2024-05-17T12:37:10Z) - Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security [5.077261736366414]
The pursuit of reliable AI systems like powerful MLLMs has emerged as a pivotal area of contemporary research.
In this paper, we endeavor to demostrate the multifaceted risks associated with the incorporation of image modalities into MLLMs.
arXiv Detail & Related papers (2024-04-08T07:54:18Z) - JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks [24.69275959735538]
This paper investigates whether techniques that successfully jailbreak Large Language Models can be equally effective in jailbreaking MLLMs.
We introduce JailBreakV-28K, a pioneering benchmark designed to assess the transferability of LLM jailbreak techniques to MLLMs.
We generate 20, 000 text-based jailbreak prompts using advanced jailbreak attacks on LLMs, alongside 8, 000 image-based jailbreak inputs from recent MLLMs jailbreak attacks.
arXiv Detail & Related papers (2024-04-03T19:23:18Z) - Exploring the Reasoning Abilities of Multimodal Large Language Models
(MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning [44.12214030785711]
We review the existing evaluation protocols of multimodal reasoning, categorize and illustrate the frontiers of Multimodal Large Language Models (MLLMs)
We introduce recent trends in applications of MLLMs on reasoning-intensive tasks and discuss current practices and future directions.
arXiv Detail & Related papers (2024-01-10T15:29:21Z) - A Survey on Multimodal Large Language Models [71.63375558033364]
Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot.
This paper aims to trace and summarize the recent progress of MLLMs.
arXiv Detail & Related papers (2023-06-23T15:21:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.