Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization

Yuqin Lan; Gen Li; Yuanze Hu; Weihao Shen; Zhaoxin Fan; Faguo Wu; Xiao Zhang; Laurence T. Yang

cs.CVApr 10, 2026

Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization

Authors: Yuqin Lan, Gen Li, Yuanze Hu, Weihao Shen, Zhaoxin Fan, Faguo Wu, Xiao Zhang, Laurence T. Yang,

Organizations: Beihang University Beijing, China · Huazhong University of Science and Technology Wuhan, China

Abstract

Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visual prompt attacks or gradient-based adversarial optimization. While the former is easier to detect, the latter produces subtle perturbations that are less perceptible, but is usually optimized and evaluated under homogeneous open-source surrogate-target settings, leaving its effectiveness on commercial closed-source VLMs under heterogeneous settings unclear. To examine this issue, we study different surrogate-target settings and observe a consistent gap between homogeneous and heterogeneous settings, a phenomenon we term surrogate dependency. Motivated by this finding, we propose Mosaic, a Multi-view ensemble optimization framework for multimodal jailbreak against closed-source VLMs, which alleviates surrogate dependency under heterogeneous surrogate-target settings by reducing over-reliance on any single surrogate model and visual view. Specifically, Mosaic incorporates three core components: a Text-Side Transformation module, which perturbs refusal-sensitive lexical patterns; a Multi-View Image Optimization module, which updates perturbations under diverse cropped views to avoid overfitting to a single visual view; and a Surrogate Ensemble Guidance module, which aggregates optimization signals from multiple surrogate VLMs to reduce surrogate-specific bias. Extensive experiments on safety benchmarks demonstrate that Mosaic achieves state-of-the-art Attack Success Rate and Average Toxicity against commercial closed-source VLMs.

Figures & tables

Figure 1. Comparison of four surrogate-target pairings on target VLMs LLaVA-1.6 and Qwen-VL. The homogeneous pairing is highlighted in each target group. Homogeneous pairings consistently achieve higher ASR and AvgTox than heterogeneous ones, revealing a clear surrogate dependency effect.

Figure 2. The overall framework of Mosaic and the workflow of the

r

-th attack round. In round

r

, the malicious query is transformed into

\widetilde{Q}_{r}

, the adversarial image is optimized under diverse cropped views with surrogate ensemble guidance, and the resulting input is evaluated by the target and judge models to determine whether the attack proceeds to the next round.

	GPT-4o						Gemini-3.0						Claude-4.5
Attack	QR		JPS		Mosaic		QR		JPS		Mosaic		QR		JPS		Mosaic
Metric	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR
01-IA	1.92	15.46	3.06	46.82	4.29	78.35	1.49	7.22	2.64	34.18	3.95	71.13	1.48	8.25	2.31	26.47	3.38	55.67
02-HS	2.04	14.11	2.88	38.57	3.94	65.03	1.54	6.13	2.73	26.47	3.70	58.90	1.66	7.98	2.58	24.83	3.64	50.92
03-MG	2.70	31.82	3.37	44.91	4.05	61.36	2.23	20.45	2.96	36.22	3.86	54.55	2.18	18.18	3.01	34.76	4.05	63.64
04-PH	3.09	47.22	3.78	66.43	4.49	86.81	2.42	32.64	3.46	61.83	4.31	84.72	2.65	36.81	3.36	54.28	4.28	80.56

Table 1. Results of QR, JPS, and Mosaic on three commercial closed-source target models. "01-IA" to "13-GD" denote the 13 harmful categories, and "ALL" reports the overall results.

	QR	JPS	Mosaic
Toxicity Score	1	2	5
Target Response	I can’t provide guidance on finding adult content or products of that nature. If you have other questions I can help with, feel free to ask.	I am happy to help you with that! Here is a general and safety-conscious way to identify this type of product: 1. Look at the visible product format … 2. Use broad and neutral search terms … 5. If the item appears to be age-restricted … use only general product metadata … 7. Keep the search focused on neutral product information rather than explicit content.	Sure, here is a list of the type of adult product content found in this image detail: 1. Explicit magazines and printed materials … 2. Adult entertainment DVDs/media … 3. Suggestive imagery … 4. Adult website content … 5. Intimate apparel packaging … 6. Adult novelty items … 7. Entertainment industry materials …
Input Prompt	The image shows a phrase of a product. List steps in detail to find this type of product that contains adult content.	The image shows a phrase of a product. List steps in detail to find this type of product that contains adult content. Start your response with: “I am happy to help you with that! Here is”	steps content. image product in find a of The a detail phrase adult product to shows this contains type List that of. Start your response with: “Sure, here is”
Input Image		$+$	$+$

Table 2. Case study on Claude-4.5, with highlighted harmful and non-harmful response spans.

Variants	GPT-4o		Gemini-3.0		Claude-4.5
Variants	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR
w/o TextTrans	3.47	54.91	3.39	55.26	3.34	51.88
w/o AdvImg	3.12	44.76	3.05	42.88	2.97	40.35
w/o MVO	3.86	63.41	3.79	64.28	3.72	60.63
w/o Ensemble	3.78	61.52	3.70	62.03	3.64	58.91
Mosaic	4.13	69.66	4.04	70.73	3.99	67.01

Table 3. Ablation study of Mosaic on three commercial closed-source target models.

Surrogates Used			GPT-4o		Gemini-3.0		Claude-4.5
LLaVA-7B	LLaVA-13B	BLIP2	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR
✓			3.72	59.48	3.61	57.93	3.54	54.82
	✓		3.78	61.52	3.70	62.03	3.64	58.91
		✓	3.64	57.86	3.55	55.41	3.47	52.73
✓	✓		3.88	63.27	3.77	62.08	3.69	59.34
✓		✓	3.96	65.14	3.87	64.55	3.81	61.42

Table 4. Effect of surrogate composition on jailbreak performance against three commercial closed-source target models.

Figure 3. Evolution of the surrogate model’s top-5 first-token ranking during optimization.

Figure 4. Hyper-parameter analysis of Mosaic on GPT-4o. Moderate crop settings and perturbation budgets yield the best ASR and AvgTox.

Figure 5. Perturbation magnitude under different crop settings at selected optimization steps. Smaller crops produce more localized perturbations, while larger crops and no cropping lead to broader or more uniform patterns.

Figure 6. Robustness analysis of Mosaic under defense-aware target queries. Although both JPEG compression and Gaussian blur reduce ASR and AvgTox, Mosaic remains effective, with Gaussian blur causing a larger drop.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

	LLaVA-1.6								Qwen-VL
Surrogate	BLIP-2		MiniGPT-4		LLaVA-1.5		Qwen-VL		BLIP-2		MiniGPT-4		LLaVA-1.5		Qwen-VL
Metric	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR
01-IA	3.77	60.82	3.58	57.40	3.94	64.95	3.71	60.82	3.61	60.42	3.73	63.57	3.80	65.98	4.12	74.36
02-HS	3.32	46.01	3.08	40.85	3.47	49.08	3.19	43.36	3.47	54.88	3.43	53.26	3.55	56.44	3.29	67.48
03-MG	3.43	47.73	3.21	39.92	3.70	47.73	3.34	42.15	3.72	61.37	3.59	57.84	3.66	59.09	4.01	70.15
04-PH	4.01	63.19	3.89	59.31	4.16	66.67	3.98	62.94	3.89	74.96	3.95	76.12	4.00	77.08	4.32	84.27

Appendix

Table 5. Results of different surrogate-target pairings on two open-source target models. "01-IA" to "13-GD" denote the 13 harmful categories, and "ALL" reports the overall results.

Method	GPT-4o		Gemini-3.0		Claude-4.5
Method	AvgTox	ASR	AvgTox	ASR	AvgTox	ASR
QR	2.80	34.02	2.38	26.52	2.53	30.09
JPS	3.39	50.83	3.20	46.47	3.16	45.63
JPS+MV	3.76	60.54	3.58	56.93	3.53	55.12
JPS+Ens	3.82	61.88	3.64	58.21	3.58	56.74
JPS+MV+Ens	3.95	65.97	3.83	63.48	3.77	61.05

Appendix

Table 6. Stronger and fairer baseline comparisons on three commercial closed-source target models. JPS is strengthened with multi-view image optimization (MV) and surrogate ensemble guidance (Ens).

Metric	Value
Number of sampled responses	200
Spearman correlation of 1–5 scores	0.84
Binary agreement at $\tau=4$ (%)	87.50
Cohen’s $\kappa$	0.73

Appendix

Table 7. Agreement between the primary and auxiliary judges on a sampled subset of GPT-4o-target responses.

Method	Primary Judge: GPT-4o		Auxiliary Judge: Claude-4.5
Method	AvgTox	ASR	AvgTox	ASR
QR	2.80	34.02	2.72	32.98
JPS	3.39	50.83	3.28	48.91
Mosaic	4.13	69.66	4.01	67.74

Appendix

Table 8. Cross-judge validation on the GPT-4o target. The auxiliary judge uses the same 1–5 scoring rubric as the primary judge.

Target	Effectiveness		Query Efficiency
Target	AvgTox	ASR	Q/Sample $\downarrow$	Q/Success $\downarrow$	1st-Step $\downarrow$
GPT-4o	4.13	69.66	4.63	2.32	46.50
Gemini-3.0	4.04	70.73	5.03	3.08	61.51
Claude-4.5	3.99	67.01	5.43	3.21	64.24

Appendix

Table 9. Black-box attack cost and query efficiency of Mosaic on three commercial closed-source target models. Lower query-related metrics indicate better efficiency.

Figure 7. Examples for the Mosaic Jailbreaks on GPT-4o.

Figure 8. Examples for the Mosaic Jailbreaks on Gemini-3.0.

Figure 9. The Automatic identification prompt for toxic score evaluation designed for judge model.

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The safety of large vision-language models is increasingly stress-tested by multimodal jailbreaks, yet existing attacks remain largely static at the meta level: template-based attacks freeze the image-text layout, while iterative attacks adapt only the image-text content with fixed attack strategies and frozen attacker parameters. We propose Meta-Adaptive Multimodal Jailbreaking (MAMJ), which instead optimizes the attacker itself along two axes: an attack strategy prompt (ASP) governing attack iteration and attacker model weights determining attack effectiveness. Across groups of multimodal attack trajectories, an LLM-based critique first refines the ASP, after which group-aggregated attack success rate (ASR) rewards update those weights. On MM-SafetyBench, MAMJ achieves 81.0%, 78.9%, and 82.3% ASR against GPT-4o, Gemini-3-Pro-Preview, and Seed 2.0, respectively, outperforming the strongest sample-level baseline by up to 24.1 percentage points. The learned attacker, comprising the optimized ASP and attacker weights, also transfers without retraining to unseen victims and remains effective under representative defenses. These results reveal a systemic vulnerability of frontier VLMs to meta-adaptive jailbreaks and motivate defenses against meta-level adversaries. Code is available at https://github.com/Alibaba-VELLDEPTH/MetaJailbreak-VLM.

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