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Jailbreak Large Vision-Language Models Through Multi-Modal Linkage
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With the significant advancement of Large Vision-Language Models (VLMs), concerns about their potential misuse and abuse have grown rapidly. Previous studies have highlighted VLMs' vulnerability to jailbreak attacks, where carefully crafted inputs can lead the model to produce content that violates ethical and legal standards. However, existing methods struggle against state-of-the-art VLMs like GPT-4o, due to the over-exposure of harmful content and lack of stealthy malicious guidance. In this work, we propose a novel jailbreak attack framework: Multi-Modal Linkage (MML) Attack. Drawing inspiration from cryptography, MML utilizes an encryption-decryption process across text and image modalities to mitigate over-exposure of malicious information. To align the model's output with malicious intent covertly, MML employs a technique called "evil alignment", framing the attack within a video game production scenario. Comprehensive experiments demonstrate MML's effectiveness. Specifically, MML jailbreaks GPT-4o with attack success rates of 97.80% on SafeBench, 98.81% on MM-SafeBench and 99.07% on HADES-Dataset. Our code is available at https://github.com/wangyu-ovo/MML.
Forward citations
Cited by 9 Pith papers
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On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
Pre-LayerNorm transformers and linear attention are almost always surjective, so any target output has an input that produces it in the continuous embedding space.
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Visual Token Compression Enhances Robustness of MLLMs
Pruning visual tokens farthest from the text feature space at selected 'robust' layers improves MLLM jailbreak defense (average +13.29% RAR) and slightly reduces hallucination.
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Multi-Turn Adaptive Prompting Attack on Large Vision-Language Models
A multi-turn attack that alternates text and image prompts and adapts its trajectory outperforms existing jailbreak methods on four vision-language models, reaching up to 100% attack success rate.
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The First Differentiable Transfer-Based Algorithm for Discrete MicroLED Repair
The submission's abstract announces a differentiable microLED repair planner, yet its full text is the LoD jailbreak-detection paper (arXiv 2508.09201v4), leaving the claimed result without any derivation.
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Backdoor Cleaning without External Guidance in MLLM Fine-tuning
BYE filters backdoored training images from MLLM fine-tuning by clustering low attention entropy across selected layers.
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Implicit Jailbreak Attacks via Cross-Modal Information Concealment on Vision-Language Models
IJA hides a malicious instruction in image steganography and uses a benign extraction prompt plus iterative template refinement to make multimodal LLMs execute it.
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Behind the Mask: Benchmarking Camouflaged Jailbreaks in Large Language Models
A benchmark of 500 camouflaged jailbreak prompts finds open-weight LLMs comply with 94% of harmful requests, but the result is confounded by task complexity and an overly permissive compliance metric.
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A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations
A survey of LVLM safety that adds a lifecycle taxonomy and new benchmark results showing Janus-Pro-7B has weaker safety than several open-source LVLMs.
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From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models
The paper argues hallucinations and jailbreaks share the same optimization dynamics and shows that defenses for one also reduce the other, but the theoretical support is largely circular.
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