Researchers Reveal Universal Encoder-Level Attack on Audio Language Models

Global: Researchers Reveal Universal Encoder-Level Attack on Audio Language Models

On December 29, 2025, a team of researchers—Roee Ziv, Raz Lapid, and Moshe Sipper—published a study describing a universal targeted latent‑space attack that manipulates the audio encoder of multimodal language models to produce attacker‑specified text outputs. The attack operates without direct access to the downstream language model and is designed to work across different audio inputs and speakers. Experiments conducted on the Qwen2‑Audio‑7B‑Instruct model demonstrated high success rates while preserving the perceptual quality of the original audio.

Background on Audio‑Language Models

Audio‑language models integrate an acoustic encoder with a large language model (LLM) to enable tasks such as transcription, translation, and multimodal reasoning. By converting raw waveforms into latent representations, the encoder supplies the LLM with a compact summary of the audio content, which the LLM then processes to generate textual responses.

Methodology of the Latent‑Space Attack

The authors propose a universal perturbation that is added to the raw audio signal before encoding. Unlike prior attacks that target specific waveforms or require knowledge of the LLM, this method learns a single perturbation vector that generalizes across inputs. The perturbation is optimized to steer the encoder’s latent output toward a region that triggers a predetermined response from the downstream LLM.

Experimental Evaluation

Using the open‑source Qwen2‑Audio‑7B‑Instruct model, the researchers evaluated attack success by measuring the frequency with which the model produced the targeted text. Results showed success rates exceeding 90 % on a diverse test set, while objective audio quality metrics indicated minimal perceptual distortion. The study also compared the encoder‑level attack to conventional waveform‑level attacks, finding comparable effectiveness with reduced computational overhead.

Implications for Model Security

The findings highlight an attack surface at the encoder stage that has received limited attention in prior security analyses of multimodal systems. Because the attack does not require access to the language model’s parameters, it could be deployed in scenarios where only the audio front‑end is exposed, such as voice‑activated assistants or transcription services.

Potential Mitigations

The authors suggest several defensive strategies, including adversarial training of the audio encoder, incorporation of detection mechanisms for anomalous latent vectors, and the use of randomized preprocessing steps to disrupt the universality of the perturbation.

Future Research Directions

Further work may explore the transferability of the universal perturbation to other audio‑LLM architectures, assess robustness against adaptive defenses, and investigate the trade‑off between attack stealthiness and success probability.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.