Adversarial Illusions in Multi-Modal Embeddings
This exposes a security flaw in multi-modal AI systems, potentially affecting all applications relying on these embeddings, though it is incremental as it builds on existing adversarial attack research.
The paper tackles the vulnerability of multi-modal embeddings to adversarial illusions, where perturbations to inputs like images or sounds can align their embeddings with arbitrary targets in other modalities, compromising tasks such as image generation and zero-shot classification across embeddings like ImageBind and Amazon's Titan.
Multi-modal embeddings encode texts, images, thermal images, sounds, and videos into a single embedding space, aligning representations across different modalities (e.g., associate an image of a dog with a barking sound). In this paper, we show that multi-modal embeddings can be vulnerable to an attack we call "adversarial illusions." Given an image or a sound, an adversary can perturb it to make its embedding close to an arbitrary, adversary-chosen input in another modality. These attacks are cross-modal and targeted: the adversary can align any image or sound with any target of his choice. Adversarial illusions exploit proximity in the embedding space and are thus agnostic to downstream tasks and modalities, enabling a wholesale compromise of current and future tasks, as well as modalities not available to the adversary. Using ImageBind and AudioCLIP embeddings, we demonstrate how adversarially aligned inputs, generated without knowledge of specific downstream tasks, mislead image generation, text generation, zero-shot classification, and audio retrieval. We investigate transferability of illusions across different embeddings and develop a black-box version of our method that we use to demonstrate the first adversarial alignment attack on Amazon's commercial, proprietary Titan embedding. Finally, we analyze countermeasures and evasion attacks.