13 citations · 53 across the 29 of their papers we have counts for
10 papers · 1 filter
Bridging the Copyright Gap: Do Large Vision-Language Models Recognize and Respect Copyrighted Content?
Naen Xu, Jinghuai Zhang, Changjiang Li +7
Large vision-language models (LVLMs) have achieved remarkable advancements in multimodal reasoning tasks. However, their widespread accessibility raises critical concerns about pot…
You Can't Steal Nothing: Mitigating Prompt Leakages in LLMs via System Vectors
Bochuan Cao, Changjiang Li, Yuanpu Cao +3
Large language models (LLMs) have been widely adopted across various applications, leveraging customized system prompts for diverse tasks. Facing potential system prompt leakage ri…
Automatic Red Teaming LLM-based Agents with Model Context Protocol Tools
Ping He, Changjiang Li, Binbin Zhao +2
The remarkable capability of large language models (LLMs) has led to the wide application of LLM-based agents in various domains. To standardize interactions between LLM-based agen…
Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models
Yuhui Wang, Changjiang Li, Guangke Chen +2
Large reasoning models (LRMs) exhibit unprecedented capabilities in solving complex problems through Chain-of-Thought (CoT) reasoning. However, recent studies reveal that their fin…
VideoEraser: Concept Erasure in Text-to-Video Diffusion Models
Naen Xu, Jinghuai Zhang, Changjiang Li +5
The rapid growth of text-to-video (T2V) diffusion models has raised concerns about privacy, copyright, and safety due to their potential misuse in generating harmful or misleading…
Your Agent Can Defend Itself against Backdoor Attacks
Li Changjiang, Liang Jiacheng, Cao Bochuan +2
Despite their growing adoption across domains, large language model (LLM)-powered agents face significant security risks from backdoor attacks during training and fine-tuning. Thes…
VModA: An Effective Framework for Adaptive NSFW Image Moderation
Han Bao, Qinying Wang, Zhi Chen +6
Not Safe/Suitable for Work (NSFW) content is rampant on social networks and poses serious harm to citizens, especially minors. Current detection methods mainly rely on deep learnin…
On the Security Risks of ML-based Malware Detection Systems: A Survey
Ping He, Yuhao Mao, Changjiang Li +3
Malware presents a persistent threat to user privacy and data integrity. To combat this, machine learning-based (ML-based) malware detection (MD) systems have been developed. Howev…
RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models
Tanqiu Jiang, Changjiang Li, Fenglong Ma +1
Differentially private diffusion models (DPDMs) harness the remarkable generative capabilities of diffusion models while enforcing differential privacy (DP) for sensitive data. How…
GraphRAG under Fire
Jiacheng Liang, Yuhui Wang, Changjiang Li +4
GraphRAG advances retrieval-augmented generation (RAG) by structuring external knowledge as multi-scale knowledge graphs, enabling language models to integrate both broad context a…