Audio Language Models (ALMs) have recently shown strong capabilities in unified reasoning over speech, sound, and natural language; yet we find that they can inherit sycophancy, the tendency to agree with user assertions even when they contradict objective evidence. This failure mode is especially concerning for audio-conditioned reasoning, where a model must preserve evidence from acoustic events, speaker characteristics, and speech rate while responding to potentially misleading user feedback. However, unlike text and vision-language sycophancy, ALM sycophancy has not been systematically studied. We therefore introduce SYAUDIO, the first benchmark dedicated to evaluating sycophancy in ALMs, consisting of 4,319 audio questions spanning Audio Perception, Audio Reasoning, Audio Math, and Audio Ethics. Built upon established audio benchmarks and augmented with TTS-generated arithmetic and moral reasoning tasks, SYAUDIO enables systematic evaluation across multiple domains and sycophancy types with a human-speaker validation. Using this benchmark, we identify substantial and audio-specific sycophancy patterns under realistic conditions involving noise and speech rate, and further show that supervised fine-tuning reduces misleading susceptibility while decode-time steering reveals controllable hidden-state directions.

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