1 citations · 4 across the 12 of their papers we have counts for
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Polar: LLM-Powered Synthesis of Real-World Cyber Evidence for Prioritization and Mitigation
Luoxi Tang, Yuqiao Meng, Ankita Patra +3
Cyber threat analysis increasingly depends on evidence distributed across vendor advisories, vulnerability databases, and threat intelligence sources. Turning these fragmented obse…
POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment
Luoxi Tang, Yuqiao Meng, Ankita Patra +3
Large Language Models (LLMs) are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs offer cyber threat intelligence…
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…
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…