Universal Targeted Attack on Audio-Language Models
Analysis
This paper identifies a critical vulnerability in audio-language models, specifically at the encoder level. It proposes a novel attack that is universal (works across different inputs and speakers), targeted (achieves specific outputs), and operates in the latent space (manipulating internal representations). This is significant because it highlights a previously unexplored attack surface and demonstrates the potential for adversarial attacks to compromise the integrity of these multimodal systems. The focus on the encoder, rather than the more complex language model, simplifies the attack and makes it more practical.
Key Takeaways
- •Identifies a vulnerability in audio-language models at the encoder level.
- •Proposes a universal, targeted, latent-space attack.
- •Attack generalizes across inputs and speakers.
- •Demonstrates high attack success rates with minimal distortion.
- •Highlights a previously underexplored attack surface.
“The paper demonstrates consistently high attack success rates with minimal perceptual distortion, revealing a critical and previously underexplored attack surface at the encoder level of multimodal systems.”