1 citations · 3 across the 7 of their papers we have counts for
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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…
Robustifying Safety-Aligned Large Language Models through Clean Data Curation
Xiaoqun Liu, Jiacheng Liang, Muchao Ye +1
Large language models (LLMs) are vulnerable when trained on datasets containing harmful content, which leads to potential jailbreaking attacks in two scenarios: the integration of…