Enhancing Visual Task-Redirection Attacks on Vision-Language-Action Models Via Closed-Loop Optimization

Vision-language-action VLA models enable language-guided robot manipulation, but remain vulnerable to visual attacks that undermine user control over robot behavior. Prior studies optimize visual perturbations using fixed target supervision. However, the perturbation can change earlier actions an...

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models

While vision and multimodal foundation models underpin critical tasks from perception to complex reasoning, they remain highly vulnerable to adversarial attacks. However, traditional adversarial attacks are typically limited to single, predefined objectives, tightly coupling each attack to a...

Investigating Vulnerabilities and Defenses against Audio-Visual Attacks: a Comprehensive Survey Emphasizing Multimodal Models

Multimodal large language models MLLMs, which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual tasks. Despite the superior performance of MLLMs, the scarcity of high-quality audio-visual training data and computation...