4 citations · 4 across the 5 of their papers we have counts for
8 papers
MEvince: Knowledge-Constrained LLM Reasoning for Technique Identification from Malware Reports
Hongyu She, Yiling He, Mengda Xie +4
Mapping behavioral intelligence in malware analysis reports to ATT&CK techniques supports threat analysis and defense, but reliable identification remains challenging. Supporting e…
ParetoSaddler: Pareto-Guided Optimization of Agent Harnesses for Quality and Cost
Xingzhi Qian, Sungho Park, Rongyuan Tan +13
Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality…
APEX: Active Protection at Execution Boundaries for LLM Agents
Xinran Zheng, Xin Fan Guo, Zhiqiang Hao +7
Indirect prompt injection (IPI) hides adversarial instructions in content that large language model (LLM) agents read at runtime. As agents compose heterogeneous capability units,…
Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning
Xingzhi Qian, Xinran Zheng, Yiling He +1
Recent LLM-based systems have shown promising capabilities for security-focused code analysis. Malware understanding, however, poses a distinct challenge: analysts must reconstruct…
Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing
Xinran Zheng, Xingzhi Qian, Yiling He +2
Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them wi…
Learning to Focus: Context Extraction for Efficient Code Vulnerability Detection with Language Models
Xinran Zheng, Xingzhi Qian, Huichi Zhou +4
Language models (LMs) show promise for vulnerability detection but struggle with long, real-world code due to sparse and uncertain vulnerability locations. These issues, exacerbate…
On Benchmarking Code LLMs for Android Malware Analysis
Yiling He, Hongyu She, Xingzhi Qian +4
Large Language Models (LLMs) have demonstrated strong capabilities in various code intelligence tasks. However, their effectiveness for Android malware analysis remains underexplor…
LAMD: Context-driven Android Malware Detection and Classification with LLMs
Xingzhi Qian, Xinran Zheng, Yiling He +2
The rapid growth of mobile applications has escalated Android malware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset bia…