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BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Implementation pattern included in full analysis above.

MVP Investment

$10K - $13K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
SaaS Stack
$800
Domain & Legal
$500

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

X

Xiaohan Zhao

VILA Lab, MBZUAI

Z

Zhaoyi Li

VILA Lab, MBZUAI

Y

Yaxin Luo

VILA Lab, MBZUAI

J

Jiacheng Cui

VILA Lab, MBZUAI

Find Similar Experts

AI experts on LinkedIn & GitHub

Founder's Pitch

"Enhance security of vision-language models with highly effective black-box adversarial attack tool."

AI SecurityScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

4/4 signals

10

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

Why It Matters

This research presents a significant advancement in black-box adversarial attacks on large vision-language models (LVLMs), which are crucial in identifying and patching security vulnerabilities in AI systems that can impact applications reliant on multimedia data processing.

Product Angle

This could be developed into a security testing tool that provides insights into weaknesses within LVLMs, helping enterprises secure their AI systems from sophisticated adversarial attacks.

Disruption

It challenges existing security testing frameworks by offering a more efficient and higher success rate attack methodology, potentially replacing less effective legacy security analysis tools.

Product Opportunity

The product could cater to a rapidly expanding market of AI-driven companies keen to safeguard their systems against attacks, especially those deploying vision and language models across industries such as autonomous vehicles, content moderation, and surveillance.

Use Case Idea

A cybersecurity service that targets AI models to test and improve their resilience against adversarial attacks, marketed to companies using multimodal AI in sensitive or high-security applications.

Science

The paper enhances a known attack framework (M-Attack) by introducing finer-grained targeting through multiple novel techniques, including Multi-Crop Alignment and Auxiliary Target Alignment. These methods address issues of gradient instability by averaging across multiple randomized views in attack iterations, reducing gradient variance and improving transferability of black-box attacks on LVLMs.

Method & Eval

The study improved the success rate of black-box attacks across several current commercial LVLMs like Claude, Gemini, and GPT, demonstrating the effectiveness of their approach by outperforming existing methods.

Caveats

The reliance on cutting-edge models means it might not be as effective on more traditional or older architectures, and the approach focuses on security issues which might be rapidly patched by proactive companies.

Author Intelligence

Xiaohan Zhao

VILA Lab, MBZUAI
xiaohan.zhao@mbzuai.ac.ae

Zhaoyi Li

VILA Lab, MBZUAI
zhaoyi.li@mbzuai.ac.ae

Yaxin Luo

VILA Lab, MBZUAI
yaxin.luo@mbzuai.ac.ae

Jiacheng Cui

VILA Lab, MBZUAI
jiacheng.cui@mbzuai.ac.ae

Zhiqiang Shen

VILA Lab, MBZUAI
zhiqiang.shen@mbzuai.ac.ae

References (35)

[1]
Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment
2025Xiaojun Jia, Sensen Gao et al.
[2]
A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1
2025Zhaoyi Li, Xiaohan Zhao et al.
[3]
VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks
2024Jiannan Wu, Muyan Zhong et al.
[4]
Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models via Diffusion Models
2024Qing Guo, Shanmin Pang et al.
[5]
Enhancing Visual Question Answering through Question-Driven Image Captions as Prompts
2024Övgü Özdemir, Erdem Akagündüz
[6]
Questioning, Answering, and Captioning for Zero-Shot Detailed Image Caption
2024Duc-Tuan Luu, Viet-Tuan Le et al.
[7]
AnyAttack: Towards Large-scale Self-supervised Generation of Targeted Adversarial Examples for Vision-Language Models
2024Jiaming Zhang, Junhong Ye et al.
[8]
How Robust is Google's Bard to Adversarial Image Attacks?
2023Yinpeng Dong, Huanran Chen et al.
[9]
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
2023Lei Zhu, Tianshi Wang et al.
[10]
Image Captioners Are Scalable Vision Learners Too
2023Michael Tschannen, Manoj Kumar et al.
[11]
On Evaluating Adversarial Robustness of Large Vision-Language Models
2023Yunqing Zhao, Tianyu Pang et al.
[12]
Visual Instruction Tuning
2023Haotian Liu, Chunyuan Li et al.
[13]
DINOv2: Learning Robust Visual Features without Supervision
2023M. Oquab, Timothée Darcet et al.
[14]
Rethinking Model Ensemble in Transfer-based Adversarial Attacks
2023Huanran Chen, Yichi Zhang et al.
[15]
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
2023Junnan Li, Dongxu Li et al.
[16]
@ CREPE: Can Vision-Language Foundation Models Reason Compositionally?
2022Zixian Ma, Jerry Hong et al.
[17]
LAION-5B: An open large-scale dataset for training next generation image-text models
2022Christoph Schuhmann, R. Beaumont et al.
[18]
Bootstrap Generalization Ability from Loss Landscape Perspective
2022Huanran Chen, Shitong Shao et al.
[19]
Frequency Domain Model Augmentation for Adversarial Attack
2022Yuyang Long, Qi-li Zhang et al.
[20]
Flamingo: a Visual Language Model for Few-Shot Learning
2022Jean-Baptiste Alayrac, Jeff Donahue et al.

Showing 20 of 35 references