Feature-Aware Token Attack for Compression-Triggered Stealthy Failures in Large Vision-Language Models

Visual-token compression improves the efficiency of large vision-language models, but can expose failures that full-token evaluation misses. We study adversarial images that preserve full-token correctness yet induce errors after compression, even when both inference paths succeed on the clean image