Breaking Audio Large Language Models by Attacking Only the Encoder: A Universal Targeted Latent-Space Audio Attack
Abstract
Audio-language models combine audio encoders with large language models to enable multimodal reasoning, but they also introduce new security vulnerabilities. We propose a universal targeted latent space attack, an encoder-level adversarial attack that manipulates audio latent representations to induce attacker-specified outputs in downstream language generation. Unlike prior waveform-level or input-specific attacks, our approach learns a universal perturbation that generalizes across inputs and speakers and does not require access to the language model. Experiments on Qwen2-Audio-7B-Instruct demonstrate consistently high attack success rates with minimal perceptual distortion, revealing a critical and previously underexplored attack surface at the encoder level of multimodal systems.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a novel encoder-level adversarial attack on audio-language models, which reveals critical vulnerabilities and opens avenues for further research in security within multimodal AI systems. The paper's innovative approach and experimental results provide valuable insights into the robustness of audio-language models, although it requires improvements in reproducibility and a broader exploration of its implications.
Methodology
The proposed methodology introduces a universal targeted latent space attack that operates at the encoder level of audio-language models. This is a significant departure from traditional waveform-level or input-specific attacks, showcasing an innovative approach to adversarial attacks in multimodal systems. The authors effectively leverage latent representations to induce specific outputs, which is a novel contribution to the field. However, the paper could benefit from a more detailed explanation of the learning process for the universal perturbation and its generalization capabilities across different inputs and speakers.
Results & evaluation
The experiments conducted on the Qwen2-Audio-7B-Instruct model demonstrate high attack success rates with minimal perceptual distortion, which is commendable. The results are quantitatively presented, showing the effectiveness of the proposed attack method. However, the paper lacks a comprehensive comparison with existing attack methods, which would strengthen the claims of superiority and robustness of the proposed approach.
Reproducibility
The paper does not provide sufficient details regarding the implementation of the attack or the datasets used for training and evaluation, which raises concerns about reproducibility. Clearer guidelines or links to code repositories would enhance the ability of other researchers to replicate the results.
Limitations
One notable limitation is the lack of a thorough exploration of the attack's effectiveness across a broader range of audio-language models beyond the one tested. Additionally, the paper does not address potential countermeasures that could be employed against such attacks, which is crucial for understanding the practical implications of the research.
Broader impact
The implications of this research are significant, as it highlights a previously underexplored vulnerability in multimodal systems that combine audio and language processing. The findings could inform the development of more secure audio-language models and raise awareness about the potential risks associated with deploying such systems in real-world applications. The main contribution of this paper is the introduction of a novel encoder-level adversarial attack on audio-language models, which reveals critical vulnerabilities and opens avenues for further research in security within multimodal AI systems. The paper's innovative approach and experimental results provide valuable insights into the robustness of audio-language models, although it requires improvements in reproducibility and a broader exploration of its implications.
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