# AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

arXiv: https://arxiv.org/abs/2410.05346 · HTML: https://mlsanity.com/paper/2410.05346/

## Key facts

- Cited by 46 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 31 of its 35 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## 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 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://mlsanity.com/paper/1904.02884/) (2019)
- [FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts](https://mlsanity.com/paper/2311.05608/) (2023)
- [EVA-02: A Visual Representation for Neon Genesis](https://mlsanity.com/paper/2303.11331/) (2023)
- [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://mlsanity.com/paper/1908.06281/) (2019)
- [Visual Adversarial Examples Jailbreak Aligned Large Language Models](https://mlsanity.com/paper/2306.13213/) (2023)
- [Are aligned neural networks adversarially aligned?](https://mlsanity.com/paper/2306.15447/) (2023)
- [On Evaluating Adversarial Robustness of Large Vision-Language Models](https://mlsanity.com/paper/2305.16934/) (2023)
- [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://mlsanity.com/paper/2103.15571/) (2021)
- [Admix: Enhancing the Transferability of Adversarial Attacks](https://mlsanity.com/paper/2102.00436/) (2021)
- [Towards Adversarial Attack on Vision-Language Pre-training Models](https://mlsanity.com/paper/2206.09391/) (2022)
- [Jailbreaking Attack against Multimodal Large Language Model](https://mlsanity.com/paper/2402.02309/) (2024)
- [Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt](https://mlsanity.com/paper/2406.04031/) (2024)
- [Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models](https://mlsanity.com/paper/2307.14061/) (2023)
- [AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning](https://mlsanity.com/paper/2308.07026/) (2023)
- [VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models](https://mlsanity.com/paper/2310.04655/) (2023)
- [Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts](https://mlsanity.com/paper/2311.09127/) (2023)
- [White-box Multimodal Jailbreaks Against Large Vision-Language Models](https://mlsanity.com/paper/2405.17894/) (2024)
- [A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks](https://mlsanity.com/paper/2106.02105/) (2021)
- [Enhancing the Self-Universality for Transferable Targeted Attacks](https://mlsanity.com/paper/2209.03716/) (2022)
- [Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size](https://mlsanity.com/paper/2401.13205/) (2024)

## Built on by (most cited first, 30 of 46)

- [Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety](https://mlsanity.com/paper/2502.05206/) (2025)
- [Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving](https://mlsanity.com/paper/2501.13563/) (2025)
- [Model-agnostic Adversarial Attack and Defense for Vision-Language-Action Models](https://mlsanity.com/paper/2510.13237/) (2025)
- [ICON: Indirect Prompt Injection Defense for Agents based on Inference-Time Correction](https://mlsanity.com/paper/2602.20708/) (2026)
- [GeoShield: Safeguarding Geolocation Privacy from Vision-Language Models via Adversarial Perturbations](https://mlsanity.com/paper/2508.03209/) (2025)
- [Enhancing Targeted Adversarial Attacks on Large Vision-Language Models via Intermediate Projector](https://mlsanity.com/paper/2508.13739/) (2025)
- [Improving Generalizability and Undetectability for Targeted Adversarial Attacks on Multimodal Pre-trained Models](https://mlsanity.com/paper/2509.19994/) (2025)
- [Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models](https://mlsanity.com/paper/2411.05056/) (2024)
- [GHOST: Hallucination-Inducing Image Generation for Multimodal LLMs](https://mlsanity.com/paper/2509.25178/) (2025)
- [Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models](https://mlsanity.com/paper/2510.03721/) (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)
- [Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP](https://mlsanity.com/paper/2601.19210/) (2026)
- [Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models](https://mlsanity.com/paper/2601.22737/) (2026)
- [ZAYA1-VL-8B Technical Report](https://mlsanity.com/paper/2605.08560/) (2026)
- [Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs](https://mlsanity.com/paper/2605.21541/) (2026)
- [Steal the Patch Size: Adversarially Manipulate Vision-Language Models](https://mlsanity.com/paper/2607.00174/) (2026)
- [Do Agents Dream of False Memories? Black-box Visual Attacks on Long-term Memory in Multimodal AI Agents](https://mlsanity.com/paper/2607.15657/) (2026)
- [VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models](https://mlsanity.com/paper/2510.17759/) (2025)
- [Universal Adversarial Attacks against Closed-Source MLLMs via Target-View Routed Meta Optimization](https://mlsanity.com/paper/2601.23179/) (2026)
- [Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection](https://mlsanity.com/paper/2604.07831/) (2026)
- [Sparse Autoencoders as Plug-and-Play Firewalls for Adversarial Attack Detection in VLMs](https://mlsanity.com/paper/2605.07447/) (2026)
- [Adversarial Attacks Against MLLMs via Progressive Resolution Processing and Adaptive Feature Alignment](https://mlsanity.com/paper/2605.09902/) (2026)
- [Semantic Router: On the Feasibility of Hijacking MLLMs via a Single Adversarial Perturbation](https://mlsanity.com/paper/2511.20002/) (2025)
- [Feature-Space Smoothing: Certified Robustness of Deep Representations](https://mlsanity.com/paper/2601.16200/) (2026)
- [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)
- [Covert Visual Prompt Injection against Commercial Multimodal Large Language Models](https://mlsanity.com/paper/2603.29418/) (2026)
- [PDA: Text-Augmented Defense Framework for Robust Vision-Language Models against Adversarial Image Attacks](https://mlsanity.com/paper/2604.01010/) (2026)
- [Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patch for Infrared Vision-Language Models](https://mlsanity.com/paper/2604.03117/) (2026)

## Abstract (arXiv)

Due to their multimodal capabilities, Vision-Language Models (VLMs) have found numerous impactful applications in real-world scenarios. However, recent studies have revealed that VLMs are vulnerable to image-based adversarial attacks. Traditional targeted adversarial attacks require specific targets and labels, limiting their real-world impact.We present AnyAttack, a self-supervised framework that transcends the limitations of conventional attacks through a novel foundation model approach. By pre-training on the massive LAION-400M dataset without label supervision, AnyAttack achieves unprecedented flexibility - enabling any image to be transformed into an attack vector targeting any desired output across different VLMs.This approach fundamentally changes the threat landscape, making adversarial capabilities accessible at an unprecedented scale. Our extensive validation across five open-source VLMs (CLIP, BLIP, BLIP2, InstructBLIP, and MiniGPT-4) demonstrates AnyAttack's effectiveness across diverse multimodal tasks. Most concerning, AnyAttack seamlessly transfers to commercial systems including Google Gemini, Claude Sonnet, Microsoft Copilot and OpenAI GPT, revealing a systemic vulnerability requiring immediate attention.