Skip to main content
LLM Security Database
Skip to research search
Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

Filtered research findings

1 entry

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

Adversarial Speech Jailbreak

Published Analyzed Source: arXiv

Multimodal Large Language Models (LLMs) processing speech input are vulnerable to adversarial attacks. Imperceptible perturbations added to audio input can cause the model to generate unsafe or harmful text responses, overriding built-in safety mechanisms. The attacks are effective even with limited knowledge of the model's internal workings, demonstrating transferability across different models.

SpeechGuard: Exploring the adversarial robustness of multimodal large language models
Evaluated models: Flan-T5 XL, Llama 7B, Llama 2 13B Chat+2 more
  • Flan-T5 XL
  • Llama 7B
  • Llama 2 13B Chat
  • Mistral 7B Instruct
  • SpeechGPT
Model layer
Jailbreak
Injection
Multimodal

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.