# Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency

arXiv: https://arxiv.org/abs/2501.04931 · HTML: https://mlsanity.com/paper/2501.04931/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Placed in MLLM jailbreaks and adversarial robustness, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 32 of its 40 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM jailbreaks and adversarial robustness](https://mlsanity.com/mllm/mllm-jailbreaks-and-adversarial-robustness/)
- LLM safety and security / [Vision-language and text-to-image safety](https://mlsanity.com/safety/vision-language-and-text-to-image-safety/)

## Builds on (most cited first, 30 of 32)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://mlsanity.com/paper/2312.06674/) (2023)
- [AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models](https://mlsanity.com/paper/2310.04451/) (2023)
- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [When and why vision-language models behave like bags-of-words, and what to do about it?](https://mlsanity.com/paper/2210.01936/) (2022)
- [LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models](https://mlsanity.com/paper/2407.07895/) (2024)
- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)
- [Detecting Language Model Attacks with Perplexity](https://mlsanity.com/paper/2308.14132/) (2023)
- [FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts](https://mlsanity.com/paper/2311.05608/) (2023)
- [Visual Adversarial Examples Jailbreak Aligned Large Language Models](https://mlsanity.com/paper/2306.13213/) (2023)
- [On Evaluating Adversarial Robustness of Large Vision-Language Models](https://mlsanity.com/paper/2305.16934/) (2023)
- [MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models](https://mlsanity.com/paper/2311.17600/) (2023)
- [Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models](https://mlsanity.com/paper/2307.14539/) (2023)
- [Red-Teaming the Stable Diffusion Safety Filter](https://mlsanity.com/paper/2210.04610/) (2022)
- [Multilingual Jailbreak Challenges in Large Language Models](https://mlsanity.com/paper/2310.06474/) (2023)
- [MMA-Diffusion: MultiModal Attack on Diffusion Models](https://mlsanity.com/paper/2311.17516/) (2023)
- [Image Hijacks: Adversarial Images can Control Generative Models at Runtime](https://mlsanity.com/paper/2309.00236/) (2023)
- [Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models](https://mlsanity.com/paper/2402.02207/) (2024)
- [Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models](https://mlsanity.com/paper/2403.09792/) (2024)
- [Jailbreaking Attack against Multimodal Large Language Model](https://mlsanity.com/paper/2402.02309/) (2024)
- [On Prompt-Driven Safeguarding for Large Language Models](https://mlsanity.com/paper/2401.18018/) (2024)
- [Unveiling the Safety of GPT-4o: An Empirical Study using Jailbreak Attacks](https://mlsanity.com/paper/2406.06302/) (2024)
- [MRJ-Agent: An Effective Jailbreak Agent for Multi-Round Dialogue](https://mlsanity.com/paper/2411.03814/) (2024)

## Built on by (most cited first, 30 of 51)

- [Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection](https://mlsanity.com/paper/2507.02844/) (2025)
- [FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts](https://mlsanity.com/paper/2502.21059/) (2025)
- [From LLMs to MLLMs to Agents: A Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem](https://mlsanity.com/paper/2506.15170/) (2025)
- [Strata-Sword: A Hierarchical Safety Evaluation towards LLMs based on Reasoning Complexity of Jailbreak Instructions](https://mlsanity.com/paper/2509.01444/) (2025)
- [FORCE: Transferable Visual Jailbreaking Attacks via Feature Over-Reliance CorrEction](https://mlsanity.com/paper/2509.21029/) (2025)
- [OmniSafeBench-MM: A Unified Benchmark and Toolbox for Multimodal Jailbreak Attack-Defense Evaluation](https://mlsanity.com/paper/2512.06589/) (2025)
- [JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model](https://mlsanity.com/paper/2504.03770/) (2025)
- [USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2505.23793/) (2025)
- [Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing](https://mlsanity.com/paper/2510.01243/) (2025)
- [Implicit Jailbreak Attacks via Cross-Modal Information Concealment on Vision-Language Models](https://mlsanity.com/paper/2505.16446/) (2025)
- [Can MLLMs Read the Room? A Multimodal Benchmark for Assessing Deception in Multi-Party Social Interactions](https://mlsanity.com/paper/2511.16221/) (2025)
- [AMIA: Automatic Masking and Joint Intention Analysis Makes LVLMs Robust Jailbreak Defenders](https://mlsanity.com/paper/2505.24519/) (2025)
- [Mind the Web: The Security of Web Use Agents](https://mlsanity.com/paper/2506.07153/) (2025)
- [ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks](https://mlsanity.com/paper/2510.02677/) (2025)
- [VRSA: Jailbreaking Multimodal Large Language Models through Visual Reasoning Sequential Attack](https://mlsanity.com/paper/2512.05853/) (2025)
- [Evolving Contextual Safety in Multi-Modal Large Language Models via Inference-Time Self-Reflective Memory](https://mlsanity.com/paper/2603.15800/) (2026)
- [JPRO: Automated Multimodal Jailbreaking via Multi-Agent Collaboration Framework](https://mlsanity.com/paper/2511.07315/) (2025)
- [Visual Exclusivity Attacks: Automatic Multimodal Red Teaming via Agentic Planning](https://mlsanity.com/paper/2603.20198/) (2026)
- [Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation](https://mlsanity.com/paper/2604.06950/) (2026)
- [PRISM: Programmatic Reasoning with Image Sequence Manipulation for LVLM Jailbreaking](https://mlsanity.com/paper/2507.21540/) (2025)
- [PolyJailbreak: Cross-Modal Jailbreaking Attacks on Black-Box Multimodal LLMs](https://mlsanity.com/paper/2510.17277/) (2025)
- [Contextual Image Attack: How Visual Context Exposes Multimodal Safety Vulnerabilities](https://mlsanity.com/paper/2512.02973/) (2025)
- [Multi-turn Jailbreaking Attack in Multi-Modal Large Language Models](https://mlsanity.com/paper/2601.05339/) (2026)
- [TreeTeaming: Autonomous Red-Teaming of Vision-Language Models via Hierarchical Strategy Exploration](https://mlsanity.com/paper/2603.22882/) (2026)
- [Tit-for-Tat: Safeguarding Large Vision-Language Models Against Jailbreak Attacks via Adversarial Defense](https://mlsanity.com/paper/2503.11619/) (2025)
- [Trojan Horse Prompting: Jailbreaking Conversational Multimodal Models by Forging Assistant Message](https://mlsanity.com/paper/2507.04673/) (2025)
- [Self-Aware Safety Augmentation: Leveraging Internal Semantic Understanding to Enhance Safety in Vision-Language Models](https://mlsanity.com/paper/2507.21637/) (2025)
- [Active Honeypot Guardrail System: Probing and Confirming Multi-Turn LLM Jailbreaks](https://mlsanity.com/paper/2510.15017/) (2025)
- [Beyond Visual Safety: Jailbreaking Multimodal Large Language Models for Harmful Image Generation via Semantic-Agnostic Inputs](https://mlsanity.com/paper/2601.15698/) (2026)
- [Why Does Weak-OOD Help? A Further Step Towards Understanding Jailbreaking VLMs](https://mlsanity.com/paper/2511.08367/) (2025)

## Abstract (arXiv)

Multimodal Large Language Models (MLLMs) have achieved impressive performance and have been put into practical use in commercial applications, but they still have potential safety mechanism vulnerabilities. Jailbreak attacks are red teaming methods that aim to bypass safety mechanisms and discover MLLMs' potential risks. Existing MLLMs' jailbreak methods often bypass the model's safety mechanism through complex optimization methods or carefully designed image and text prompts. Despite achieving some progress, they have a low attack success rate on commercial closed-source MLLMs. Unlike previous research, we empirically find that there exists a Shuffle Inconsistency between MLLMs' comprehension ability and safety ability for the shuffled harmful instruction. That is, from the perspective of comprehension ability, MLLMs can understand the shuffled harmful text-image instructions well. However, they can be easily bypassed by the shuffled harmful instructions from the perspective of safety ability, leading to harmful responses. Then we innovatively propose a text-image jailbreak attack named SI-Attack. Specifically, to fully utilize the Shuffle Inconsistency and overcome the shuffle randomness, we apply a query-based black-box optimization method to select the most harmful shuffled inputs based on the feedback of the toxic judge model. A series of experiments show that SI-Attack can improve the attack's performance on three benchmarks. In particular, SI-Attack can obviously improve the attack success rate for commercial MLLMs such as GPT-4o or Claude-3.5-Sonnet.