arXiv:2605.20519v1 Announce Type: cross Abstract: Prior attacks on Audio Large Language Models (Audio LLMs) demonstrated that carefully crafted waveform-domain perturbations can force targeted adversarial outputs. As a defense mechanism against these attacks, real-world codec compression preprocessing has been studied to both detect and remove the perturbations. Yet no existing attack has demonstrated robustness against these compressions. We introduce CodecAttack, which optimizes a perturbation in a neural audio codec's continuous latent space rather than directly perturbing the audio wavefor
The rapid deployment and increasing reliance on Audio LLMs for various applications make the robustness of their security against adversarial attacks a critical and timely concern.
Sophisticated actors could exploit these vulnerabilities to manipulate audio LLMs, leading to misinformation, compromised voice authentication, or disruption of services reliant on these models.
This research introduces a novel, codec-robust attack method, shifting the adversarial threat landscape for Audio LLMs from waveform-specific to a more resilient, latent-space-optimized approach.
- · Adversarial AI researchers
- · Cybersecurity firms specializing in AI
- · Developers of Audio LLMs (without robust defenses)
- · Organizations relying on Audio LLMs for sensitive applications
Audio LLM developers will need to incorporate advanced defense mechanisms against codec-robust adversarial attacks to maintain system integrity.
The development of more resilient Audio LLM architectures could accelerate, leading to a new arms race between attackers and defenders.
Public trust in AI systems reliant on audio input might erode if these vulnerabilities become widespread without adequate mitigation.
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Read at arXiv cs.AI