1 citations · 1 across the 4 of their papers we have counts for
7 papers
Voice or Stereotype? Disentangling Acoustic and Content-Based Gender in Speech-to-Speech Models
Xiaoqun Liu, Tanu Mitra, Harshit Rajgarhia +1
Speech-to-speech (S2S) models now run inside dubbing, translation, and voice agents. Unlike text models, they hear the speaker's voice, which carries the speaker's gender. A faithf…
Benchmarking LLMs in an Embodied Environment for Blue Team Threat Hunting
Xiaoqun Liu, Feiyang Yu, Xi Li +3
As cyber threats continue to grow in scale and sophistication, blue team defenders increasingly require advanced tools to proactively detect and mitigate risks. Large Language Mode…
CyLens: Towards Reinventing Cyber Threat Intelligence in the Paradigm of Agentic Large Language Models
Xiaoqun Liu, Jiacheng Liang, Qiben Yan +5
The exponential growth of cyber threat knowledge, exemplified by the expansion of databases such as MITRE-CVE and NVD, poses significant challenges for cyber threat analysis. Secur…
DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization
Xiaoqun Liu, Weiming Qi, Qiben Yan
Large language models (LLMs) remain vulnerable to jailbreak attacks in which adversarial prompts induce harmful outputs. Existing defenses often require access to the model interna…
Data to Defense: The Role of Curation in Customizing LLMs Against Jailbreaking Attacks
Xiaoqun Liu, Jiacheng Liang, Luoxi Tang +3
Large language models (LLMs) are widely adapted for downstream applications through fine-tuning, a process named customization. However, recent studies have identified a vulnerabil…
LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models
Yupeng Su, Ziyi Guan, Xiaoqun Liu +6
Large language models (LLMs) have seen substantial growth, necessitating efficient model pruning techniques. Existing post-training pruning methods primarily measure weight importa…