1 citations · 1 across the 11 of their papers we have counts for
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SoK: Intent-Oriented Systematization of Multi-Turn LLM Jailbreaks
Siyuan Li, Aodu Wulianghai, Zehao Liu +8
Large Language Models (LLMs) are increasingly deployed in interactive settings, where user intent commonly unfolds through multi-turn dialogue. Multi-turn jailbreaks exploit this p…
GraphSteal: Structural Knowledge Stealing from Graph RAG via Traversal Reconstruction
Jinze Gu, Qinghua Mao, Xi Lin +1
Retrieval-Augmented Generation (RAG) enhances LLMs by grounding generation in query-relevant external evidence. Beyond unstructured text corpora, Graph RAG integrates knowledge gra…
Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs
Leitao Yuan, Qinghua Mao, Daizong Liu +5
Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate encoders can generalize to clo…
CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks
Siyuan Li, Zehao Liu, Xi Lin +6
As Large Language Models (LLMs) are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety concerns, especially those evolvi…
GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning
Jiani Zhu, Xi Lin, Yuxin Qi +1
Graph Prompt Learning (GPL) represents an innovative approach in graph representation learning, enabling task-specific adaptations by fine-tuning prompts without altering the under…