DE-FIVE: Detecting Malicious Image Prompts via Fourier Features and Image Vector Embeddings
Vision language models (VLMs) employ both visual and textual modalities to enable advanced vision-language inference. However, incorporating visual modalities expands the attack surface of VLMs, making them more susceptible to security threats such as adversarial perturbations and indirect prompt injection, wherein crafted malicious image prompts can elicit unintended model outputs. Existing defense methods against malicious image prompts remain insufficient as they typically demand extensive datasets for retraining or the deployment of additional, complex classifiers. Most critically, there is a profound lack of specialized defense mechanisms specifically targeting indirect prompt inject...
| Authors | Xingwei Zhong, Varun Sharma, Kar Wai Fok et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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