Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) surpassing state-of-the-art diffusion models (DMs) in both image quality (FID: 1.48 vs. 1.58) and generation speed. However, the privacy risks associated with IARs remain unexplored, raising concerns regarding their responsible deployment. To address this gap, we conduct a comprehensive privacy analysis of IARs, comparing their privacy risks to the ones of DMs as reference points. Concretely, we develop a novel membership inference attack (MIA) that achieves a remarkably high success rate in detecting training images (with a TPR@FPR=1% of 86.38% vs. 4.91% for DMs with comparable attacks). We leverage our novel MIA to provide dataset inference (DI) for IARs, and show that it requires as few as 6 samples to detect dataset membership (compared to 200 for DI in DMs), confirming a higher information leakage in IARs. Finally, we are able to extract hundreds of training data points from an IAR (e.g., 698 from VAR-d30). Our results demonstrate a fundamental privacy-utility trade-off: while IARs excel in image generation quality and speed, they are significantly more vulnerable to privacy attacks compared to DMs. This trend suggests that utilizing techniques from DMs within IARs, such as modeling the per-token probability distribution using a diffusion procedure, holds potential to help mitigating IARs' vulnerability to privacy attacks.