Large Audio-Language Models (LALMs) are increasingly deployed in real-world applications, yet their robustness against malicious audio injection remains underexplored.To address this gap, this study systematically evaluates five leading LALMs across four attack scenarios: Audio Interference Attack, Instruction Following Attack, Context Injection Attack, and Judgment Hijacking Attack.We quantitatively assess their vulnerabilities and resilience using metrics: the Defense Success Rate, Context Robustness Score, and Judgment Robustness Index.The experiments reveal significant performance disparities, with no single model demonstrating consistent robustness across all attack types.Attack effectiveness is significantly influenced by the position of the malicious content, particularly when injected at the beginning of a sequence.Furthermore, our analysis uncovers a negative correlation between a model's instruction-following capability and its robustness: models that strictly adhere to instructions tend to be more susceptible, whereas safety-aligned models exhibit greater resistance.To facilitate future research, this work introduces a comprehensive benchmark framework.Our findings underscore the critical need for integrating robustness into training pipelines and developing multi-modal defenses, ultimately facilitating the secure deployment of LALMs.The dataset used in this work is available on Hugging Face.

Due to the remarkable performance of multi-modal large language models (MLLMs) in multi-modal capabilities, multi-modal in-context learning (M-ICL) has garnered widespread attention for fast adapting MLLMs to downstream tasks. However, the vulnerability of M-ICL to attacks remains largely unexplored. In this work, we take the first step to explore the backdoor vulnerability of M-ICL, which allows the adversary only to manipulate the multi-modal demonstration examples to mislead the victim model. We propose a multi-modal backdoor strategy on M-ICL via cross-modal concept mis-matching under black-box attack setting. Extensive experimental results demonstrate that our attacks exhibit high attack effectiveness while preserving the normal functionality of the victim model. Moreover, we further conduct experiments to prove our attacks are robust against backdoor defenses and still remain effective in various real-world conditions.