Feature-Aware Token Attack for Compression-Triggered Stealthy Failures in Large Vision-Language Models
cs.CV
Submitted: 2026-09-30
Updated: 2026-09-30
Terminology
Sources
- LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
- JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model
- EvoComp: Learning Visual Token Compression for Multimodal Large Language Models via Semantic-Guided Evolutionary Labeling
- InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
- Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
- Less Is More -- Until It Breaks: Security Pitfalls of Vision Token Compression in Large Vision-Language Models
- On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression
- SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
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