1 citations · 1 across the 11 of their papers we have counts for
13 papers
Before Agent Tells The Lie: Has Deception Already Been Represented?
Xinling Li, Dadi Guo, Qingyu Liu +5
Large language model (LLM)-based agents can exhibit deceptive behavior during task execution, including hiding failures, fabricating results, or falsely signaling task completion.…
SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment
Qinghua Mao, Wanying Qu, Dadi Guo +8
The performance of LLM-based agents is jointly shaped by the base model and the harness used when interacting with the environment. This exposes them to safety risks in both harmfu…
SHE: Trajectory-driven Safety Harness Evolution for LLM Agents
Wanying Qu, Qinghua Mao, Yu Li +12
The safety of large language model (LLM) agents depends not only on model weights but also on the agent harness that manages context, memory, tools, permissions, and runtime contro…
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…
AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security
Dongrui Liu, Yu Li, Zhonghao Yang +47
Modern open-world agents such as OpenClaw exhibit powerful cross-environment execution capabilities yet introduce broad new safety risk sources. Meanwhile, advanced frontier AI mod…
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…
Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing
Qinghua Mao, Xi Lin, Jinze Gu +3
Large language models (LLMs) increasingly rely on knowledge editing to support knowledge-intensive reasoning, but this flexibility also introduces critical safety risks: adversarie…
Lightweight Stylistic Consistency Profiling: Robust Detection of LLM-Generated Textual Content for Multimedia Moderation
Siyuan Li, Aodu Wulianghai, Xi Lin +6
The increasing prevalence of Large Language Models (LLMs) in content creation has made distinguishing human-written textual content from LLM-generated counterparts a critical task…
DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis
Siyuan Li, Aodu Wulianghai, Guangyan Li +5
The rapid advancement of large language models (LLMs) presents new security challenges, particularly in detecting machine-generated text used for misinformation, impersonation, and…
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…
Towards Multi-Task Generative-AI Edge Services with an Attention-based Diffusion DRL Approach
Yaju Liu, Xi Lin, Siyuan Li +3
As an emerging paradigm of content creation, AI-Generated Content (AIGC) has been widely adopted by a large number of edge end users. However, the requests for generated content fr…