  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
