arXiv AI June 2, 2026 By Yupeng Chen, Junchi Yu, Aoxi Liu, Baoyuan Wu, Philip Torr, Adel Bibi

The Alignment Curse: Modality Alignment Supercharges Audio Attacks via Text Transfer

arXiv:2602. 02557v2 Announce Type: replace-cross Abstract: Recent advances in end-to-end trained omni-models have substantially improved audio capabilities by strengthening text-audio modality alignment.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

safety

arXiv Machine Learning

Jul 1

On Optimizing Multimodal Jailbreaks for Spoken Language Models

arXiv:2603. 19127v2 Announce Type: replace Abstract: As Spoken Language Models (SLMs) integrate speech and text modalities, they inherit the safety vulnerabilities of their LLM backbone while introducing an expanded attack surface.

By Aravind Krishnan, Karolina Sta\'nczak, Dietrich Klakow

llms multimodal benchmarks safety

arXiv AI

Jun 2

Jailbreaking Multimodal Large Language Models using Multi-Clip Video

arXiv:2606. 02111v1 Announce Type: cross Abstract: As multimodal large language models (MLLMs) have advanced to process video inputs, concerns have emerged about their potential for malicious misuse.

By Choongwon Kang, Seungjong Sun, Hyunmin Jun, Jang Hyun Kim

llms multimodal safety

arXiv AI

Aug 12

Never Stop Speaking: a Denial-of-Service Attack on End-to-End Speech Language Models

arXiv:2608. 10405v1 Announce Type: cross Abstract: Many studies have shown that specially crafted inputs can induce large language models (LLMs) to generate excessively long outputs, resulting in significant computational overhead and resource consumption.

By Shuozhe Cheng, Kunlan Xiang, Mingxuan Li, Ji Zhang, Dongxiao Liu, Wenbo Jiang

llms robotics multimodal safety

arXiv AI

Aug 10

CASA: Classification Augmented with Safety Attention for Robust Multimodal Alignment

arXiv:2604. 00310v2 Announce Type: replace-cross Abstract: Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions.

By Anurag Kumar, Raghuveer Peri, Jon Burnsky, Alexandru Nelus, Rohit Paturi, Srikanth Vishnubhotla, Yanjun Qi

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 16

Discrete optimal transport is a strong audio adversarial attack

arXiv:2509. 14959v3 Announce Type: replace-cross Abstract: In this paper, we investigate discrete optimal transport (DOT) as a black-box attack against modern automatic speaker verification (ASV) and anti-spoofing countermeasure (CM) systems.

By Anton Selitskiy, Akib Shahriyar, Jishnuraj Prakasan

rag fine-tuning safety

arXiv AI

Jun 29

Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models

arXiv:2606. 28153v1 Announce Type: cross Abstract: Jailbreak attacks bypass LLM safety alignment, yet their mechanisms remain poorly understood.

By Yanchen Yin, Dongqi Han, Linghui Li

llms benchmarks safety

Related stories

On Optimizing Multimodal Jailbreaks for Spoken Language Models

Jailbreaking Multimodal Large Language Models using Multi-Clip Video

Never Stop Speaking: a Denial-of-Service Attack on End-to-End Speech Language Models

CASA: Classification Augmented with Safety Attention for Robust Multimodal Alignment

Discrete optimal transport is a strong audio adversarial attack

Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models