# X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP

arXiv: https://arxiv.org/abs/2505.05528 · HTML: https://mlsanity.com/paper/2505.05528/

## Key facts

- Cited by 21 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 122 of its 148 references that are in the map.
- First posted on arXiv 8 May 2025.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM jailbreaks and adversarial robustness](https://mlsanity.com/mllm/mllm-jailbreaks-and-adversarial-robustness/)

## Builds on (most cited first, 30 of 122)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)

## Built on by (most cited first, 21 of 21)

- [DualTAP: A Dual-Task Adversarial Protector for Mobile MLLM Agents](https://mlsanity.com/paper/2511.13248/) (2025)
- [TRAP: Targeted Redirecting of Agentic Preferences](https://mlsanity.com/paper/2505.23518/) (2025)
- [BackdoorVLM: A Benchmark for Backdoor Attacks and Defenses on Vision-Language Models](https://mlsanity.com/paper/2511.18921/) (2025)
- [Poisoning Prompt-Guided Sampling in Video Large Language Models](https://mlsanity.com/paper/2509.20851/) (2025)
- [Toward Universal and Transferable Jailbreak Attacks on Vision-Language Models](https://mlsanity.com/paper/2602.01025/) (2026)
- [CAVALRY-V: A Large-Scale Generator Framework for Adversarial Attacks on Video MLLMs](https://mlsanity.com/paper/2507.00817/) (2025)
- [HV-Attack: Hierarchical Visual Attack for Multimodal Retrieval Augmented Generation](https://mlsanity.com/paper/2511.15435/) (2025)
- [VISOR++: Universal Visual Inputs based Steering for Large Vision Language Models](https://mlsanity.com/paper/2509.25533/) (2025)
- [Universal Adversarial Attacks against Closed-Source MLLMs via Target-View Routed Meta Optimization](https://mlsanity.com/paper/2601.23179/) (2026)
- [Right Predictions, Misleading Explanations: On the Vulnerability of Vision-Language Model Explanations](https://mlsanity.com/paper/2605.16651/) (2026)
- [XPlainVerse: A Million-Scale Benchmark for Explainable Deepfake Detection](https://mlsanity.com/paper/2607.03562/) (2026)
- [Cross-Modal Content Optimization for Steering Web Agent Preferences](https://mlsanity.com/paper/2510.03612/) (2025)
- [Stage-wise Attention-Guided Region Sequencing for Adversarial Attacks on Large Vision-Language Models](https://mlsanity.com/paper/2602.04356/) (2026)
- [What Makes VLMs Robust? Towards Reconciling Robustness and Accuracy in Vision-Language Models](https://mlsanity.com/paper/2603.12799/) (2026)
- [Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patch for Infrared Vision-Language Models](https://mlsanity.com/paper/2604.03117/) (2026)
- [DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models](https://mlsanity.com/paper/2605.18868/) (2026)
- [Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards](https://mlsanity.com/paper/2608.07535/) (2026)
- [Generating Multi-view Adversarial Examples for Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2608.20748/) (2026)
- [One Attack to Fool Them All: Highly Transferable Black-Box Adversarial Attacks on Frontier MLLMs](https://mlsanity.com/paper/2609.33833/) (2026)
- [VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation](https://mlsanity.com/paper/2609.35028/) (2026)
- [Does Target Alignment Mean Target Recovery? An Evidence-Ladder Study of Adversarial Claims on Contrastive Encoders](https://mlsanity.com/paper/2610.11938/) (2026)

## Abstract (arXiv)

As Contrastive Language-Image Pre-training (CLIP) models are increasingly adopted for diverse downstream tasks and integrated into large vision-language models (VLMs), their susceptibility to adversarial perturbations has emerged as a critical concern. In this work, we introduce X-Transfer, a novel attack method that exposes a universal adversarial vulnerability in CLIP. X-Transfer generates a Universal Adversarial Perturbation (UAP) capable of deceiving various CLIP encoders and downstream VLMs across different samples, tasks, and domains. We refer to this property as super transferability--a single perturbation achieving cross-data, cross-domain, cross-model, and cross-task adversarial transferability simultaneously. This is achieved through surrogate scaling, a key innovation of our approach. Unlike existing methods that rely on fixed surrogate models, which are computationally intensive to scale, X-Transfer employs an efficient surrogate scaling strategy that dynamically selects a small subset of suitable surrogates from a large search space. Extensive evaluations demonstrate that X-Transfer significantly outperforms previous state-of-the-art UAP methods, establishing a new benchmark for adversarial transferability across CLIP models. The code is publicly available in our hrefhttps://github.com/HanxunH/XTransferBenchGitHub repository.