6 papers
Feature-Aware Token Attack for Compression-Triggered Stealthy Failures in Large Vision-Language Models
Shilinlu Yan, Bowen Chen, Yuechen Zhang +3
Visual-token compression improves the efficiency of large vision-language models, but can expose failures that full-token evaluation misses. We study adversarial images that preser…
Still There, No Longer Seen: Exposing Compression-Induced Risk in Large Vision-Language Models
Qiankun Li, Yuechen Zhang, Bowen Chen +4
Visual token compression reduces the inference cost of Large Vision-Language Models (LVLMs). However, aggregate robustness measures do not reveal whether a particular adversarial f…
SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models
Yuanhe Zhang, Jiayu Tian, Yibo Zhang +5
Large Audio Language Models (LALMs) have been widely applied in real-time scenarios, such as in-car assistants and online meeting comprehension. In practice, audio inputs are often…
CSSBench: Evaluating the Safety of Lightweight LLMs against Chinese-Specific Adversarial Patterns
Zhenhong Zhou, Shilinlu Yan, Chuanpu Liu +3
Large language models (LLMs) are increasingly deployed in cost-sensitive and on-device scenarios, and safety guardrails have advanced mainly in English. However, real-world Chinese…
RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios
Yibo Zhang, Liang Lin, Kaiwen Luo +8
While Audio Large Models (ALMs) have achieved remarkable proficiency, their robustness remains brittle in real-world deployment. Existing evaluations largely rely on synthetic Gaus…
DemonAgent: Dynamically Encrypted Multi-Backdoor Implantation Attack on LLM-based Agent
Pengyu Zhu, Zhenhong Zhou, Yuanhe Zhang +3
As LLM-based agents become increasingly prevalent, backdoors can be implanted into agents through user queries or environment feedback, raising critical concerns regarding safety v…