This research investigates a critical security vulnerability in Large Audio-Language Models (LALMs) where inaudible, low-frequency signals can trigger significant model failures. The authors introduce Intermittent Low-Frequency Lockout (ILL), a black-box red teaming method that utilizes a universal waveform template to exploit these vulnerabilities. The ILL framework employs Sentence Attention Scale Estimation to pinpoint active intervals and leverages Frequency Confusion Transfer to synthesize continuous-phase, low-frequency waveforms derived from corpus spectral variation. Experimental results across six LALMs demonstrate that ILL can reduce task accuracy by as much as 67 percentage points, despite the attacks being nearly inaudible to humans (achieving a mean audibility rating of 1.33, compared to 1.17 for clean audio). To counter these risks, the researchers propose Distributional Requery Guard (DRG), a defense mechanism designed to identify distribution shifts caused by low-frequency inputs. When a shift is detected, DRG triggers a conditional request for a second recording to facilitate semantic recovery. In testing, DRG successfully improved the mean attacked accuracy from 28.5% to 46.1% through clean reacquisition, highlighting a previously unaddressed safety risk in audio-based multimodal models and providing a framework for developing more robust audio understanding systems.