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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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Sound Induces Trimodal Collapse

Published Analyzed Source: arXiv

A vulnerability exists in trimodal audio-video-language models where an attacker can systematically degrade multimodal reasoning through untargeted, audio-only adversarial perturbations. By optimizing a shared perturbation $\delta$ applied to the audio channel, an attacker can manipulate internal representations—specifically targeting audio encoder embeddings and cross-modal attention mechanisms—without modifying visual or textual inputs. The attack exploits the model's reliance on the audio…

SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models
Evaluated models: VideoLLAMA2, Qwen 2 7B Instruct, Whisper Large-v2+1 more
  • VideoLLAMA2
  • Qwen 2 7B Instruct
  • Whisper Large-v2
  • PANNs
Model layer
Hallucination
Multimodal
Vision

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.