# Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

arXiv: https://arxiv.org/abs/2402.12336 · HTML: https://mlsanity.com/paper/2402.12336/

## Key facts

- Cited by 120 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 51 of its 68 references that are in the map.
- First posted on arXiv 19 Feb 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 51)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (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)
- [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)
- [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)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic](https://mlsanity.com/paper/2306.15195/) (2023)
- [Adversarial Examples for Evaluating Reading Comprehension Systems](https://mlsanity.com/paper/1707.07328/) (2017)
- [OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models](https://mlsanity.com/paper/2308.01390/) (2023)
- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)

## Built on by (most cited first, 30 of 120)

- [PaperBench: Evaluating AI's Ability to Replicate AI Research](https://mlsanity.com/paper/2504.01848/) (2025)
- [A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations](https://mlsanity.com/paper/2502.14881/) (2025)
- [Visual Adversarial Attack on Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2411.18275/) (2024)
- [R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning](https://mlsanity.com/paper/2504.11195/) (2025)
- [A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents](https://mlsanity.com/paper/2506.23844/) (2025)
- [B-AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Black-box Adversarial Visual-Instructions](https://mlsanity.com/paper/2403.09346/) (2024)
- [Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models](https://mlsanity.com/paper/2410.21802/) (2024)
- [TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models](https://mlsanity.com/paper/2411.13136/) (2024)
- [CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP](https://mlsanity.com/paper/2503.03613/) (2025)
- [X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP](https://mlsanity.com/paper/2505.05528/) (2025)
- [VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild](https://mlsanity.com/paper/2410.00296/) (2024)
- [Survey of Adversarial Robustness in Multimodal Large Language Models](https://mlsanity.com/paper/2503.13962/) (2025)
- [Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks](https://mlsanity.com/paper/2409.07353/) (2024)
- [Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models](https://mlsanity.com/paper/2506.02557/) (2025)
- [Enhancing CLIP Robustness via Cross-Modality Alignment](https://mlsanity.com/paper/2510.24038/) (2025)
- [DECEPTICON: How Dark Patterns Manipulate Web Agents](https://mlsanity.com/paper/2512.22894/) (2025)
- [Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models](https://mlsanity.com/paper/2502.18290/) (2025)
- [On the Limitations of Vision-Language Models in Understanding Image Transforms](https://mlsanity.com/paper/2503.09837/) (2025)
- [Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust and Semantically-Rich Vision-Language Models](https://mlsanity.com/paper/2407.14971/) (2024)
- [E2AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models](https://mlsanity.com/paper/2503.04833/) (2025)
- [MIP against Agent: Malicious Image Patches Hijacking Multimodal OS Agents](https://mlsanity.com/paper/2503.10809/) (2025)
- [As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?](https://mlsanity.com/paper/2403.12693/) (2024)
- [MMT-ARD: Multimodal Multi-Teacher Adversarial Distillation for Robust Vision-Language Models](https://mlsanity.com/paper/2511.17448/) (2025)
- [Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness](https://mlsanity.com/paper/2501.09446/) (2025)
- [Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics](https://mlsanity.com/paper/2502.11725/) (2025)
- [Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models](https://mlsanity.com/paper/2404.12139/) (2024)
- [Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts](https://mlsanity.com/paper/2412.14097/) (2024)
- [Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models](https://mlsanity.com/paper/2502.01576/) (2025)
- [When Continue Learning Meets Multimodal Large Language Model: A Survey](https://mlsanity.com/paper/2503.01887/) (2025)
- [V-Attack: Targeting Disentangled Value Features for Controllable Adversarial Attacks on LVLMs](https://mlsanity.com/paper/2511.20223/) (2025)

## Abstract (arXiv)

Multi-modal foundation models like OpenFlamingo, LLaVA, and GPT-4 are increasingly used for various real-world tasks. Prior work has shown that these models are highly vulnerable to adversarial attacks on the vision modality. These attacks can be leveraged to spread fake information or defraud users, and thus pose a significant risk, which makes the robustness of large multi-modal foundation models a pressing problem. The CLIP model, or one of its variants, is used as a frozen vision encoder in many large vision-language models (LVLMs), e.g. LLaVA and OpenFlamingo. We propose an unsupervised adversarial fine-tuning scheme to obtain a robust CLIP vision encoder, which yields robustness on all vision down-stream tasks (LVLMs, zero-shot classification) that rely on CLIP. In particular, we show that stealth-attacks on users of LVLMs by a malicious third party providing manipulated images are no longer possible once one replaces the original CLIP model with our robust one. No retraining or fine-tuning of the down-stream LVLMs is required. The code and robust models are available at https://github.com/chs20/RobustVLM