This paper introduces Chameleon, an adaptive adversarial framework that exploits image downscaling vulnerabilities in Vision-Language Models (VLMs) to inject hidden malicious visual prompts. By employing an iterative, feedback-driven optimization mechanism, Chameleon can craft imperceptible perturbations that hijack VLM execution and compromise agentic decision-making systems.

DraCo introduces a novel interleaved reasoning paradigm, Draft-as-CoT, for text-to-image generation that leverages both textual and visual content. This approach addresses limitations of existing methods by generating low-resolution draft images for visual planning and verification, significantly improving the generation of rare attribute combinations and overall image quality.