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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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1 entry

Matches every word across titles, descriptions, sources, affected systems, and models.

Airborne Audio LLM Hijack

Published Analyzed Source: arXiv

Audio-based Large Language Models (ALLMs), specifically Qwen2-Audio, are vulnerable to over-the-air adversarial audio attacks. An attacker with white-box access can generate robust adversarial audio perturbations using gradient-based optimization combined with audio augmentation techniques (specifically SpecAugment, translation, and additive noise). These perturbations, when played through a speaker in the physical environment, manipulate the ALLM processing the audio via a microphone. This…

Attacker's Noise Can Manipulate Your Audio-based LLM in the Real World
Evaluated models: Not reported
Model layer
Injection
Jailbreak
Denial of service

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.