1 citations · 2 across the 8 of their papers we have counts for
9 papers
SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs
Wenyu Chen, Li Wang, Chuanchao Zang +5
Video Multimodal Large Language Models (Video-MLLMs) support reasoning over video inputs, yet remain vulnerable to jailbreak attacks that elicit policy-violating responses. Existin…
Transferable End-to-End Optimization for Indirect Long-Term Memory Poisoning in LLM Agents
Chuanchao Zang, Jianing Wang, Wenyu Chen +5
Long-term memory can turn untrusted external content into persistent influence over an LLM agent's future decisions, creating the threat of indirect memory poisoning. A successful…
Extracting Knowledge from Tools in LLM Agents
Chuanchao Zang, Jianing Wang, Wenyu Chen +6
LLM agents commonly use knowledge-based tools and access their underlying files, databases, and search indexes through tool invocation. This integration improves agents' ability to…
Understanding Stage-Wise Utility-Risk Trade-offs in LLM Agent Memory
Chuanchao Zang, Zijian Cao, Xiangtao Meng +6
Long-term memory is becoming a core capability of LLM agents, enabling personalization and long-horizon interaction. However, memory mechanisms that retain, transform, or expose mo…
Isolated but Exposed: Persistence-Based Memory Extraction Attack on LLM Agents
Xinyu Gao, Wenyu Chen, Xiangtao Meng +5
LLM-based agents extend large language models with long-term memory (LTM) that persists privacy-sensitive user data across sessions. Production systems mitigate extraction risks th…
Defenses at Odds: Measuring and Explaining Defense Conflicts in Large Language Models
Xiangtao Meng, Wenyu Chen, Chuanchao Zang +5
Large language models (LLMs) may require additional defenses after deployment as risks and governance requirements evolve. Subsequent defenses can interact with earlier defenses, r…
OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models
Junda Wu, Xintong Li, Ruoyu Wang +9
Offline evaluation of LLMs is crucial in understanding their capacities, though current methods remain underexplored in existing research. In this work, we focus on the offline eva…
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation
Junda Wu, Cheng-Chun Chang, Tong Yu +4
The long-tail recommendation is a challenging task for traditional recommender systems, due to data sparsity and data imbalance issues. The recent development of large language mod…
InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment
Jianing Wang, Junda Wu, Yupeng Hou +3
Do current large language models (LLMs) better solve graph reasoning and generation tasks with parameter updates? In this paper, we propose InstructGraph, a framework that empowers…