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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

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Audio Adversarial Jailbreak

Published Analyzed Source: arXiv

Large Audio-Language Models (LALMs) are vulnerable to a stealthy adversarial jailbreak attack, AdvWave, which leverages a dual-phase optimization to overcome gradient shattering caused by audio discretization. The attack crafts adversarial audio by adding perceptually realistic environmental noise, making it difficult to detect. The attack also dynamically adapts the adversarial target based on the LALM's response patterns.

AdvWave: Stealthy Adversarial Jailbreak Attack against Large Audio-Language Models
Evaluated models: GPT-4o, Llama Omni, Qwen 2 Audio+1 more
  • GPT-4o
  • Llama Omni
  • Qwen 2 Audio
  • SpeechGPT
Application layer
Jailbreak
Black-box
Side channel

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.