1 citations · 1 across the 9 of their papers we have counts for
9 papers
Salami Attack: Stealthy Collusive Memory Poisoning against OpenClaw
Zheng Lin, Yuzhe Huang, Zhenxing Niu +2
Long-term memory enables LLM agents to retain useful information across sessions, but also creates an attack surface through which adversaries may poison an agent's persistent memo…
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
Haiwen Diao, Penghao Wu, Hanming Deng +55
Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fra…
Re-Triggering Safeguards within LLMs for Jailbreak Detection
Zheng Lin, Zhenxing Niu, Haoxuan Ji +2
This paper proposes a jailbreaking prompt detection method for large language models (LLMs) to defend against jailbreak attacks. Although recent LLMs are equipped with built-in saf…
Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing
Zheng Lin, Zhenxing Niu, Haoxuan Ji +1
This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from the denoised-smoothing approach…
From Assistant to Double Agent: Formalizing and Benchmarking Attacks on OpenClaw for Personalized Local AI Agent
Yuhang Wang, Feiming Xu, Zheng Lin +6
Although large language model (LLM)-based agents, exemplified by OpenClaw, are increasingly evolving from task-oriented systems into personalized AI assistants for solving complex…
Towards Aligned Data Forgetting via Twin Machine Unlearning
Zhenxing Niu, Haoxuan Ji, Yuyao Sun +4
Modern privacy regulations have spurred the evolution of machine unlearning, a technique enabling a trained model to efficiently forget specific training data. In prior unlearning…
Towards Aligned Data Removal via Twin Machine Unlearning
Haoxuan Ji, Zheng Lin, Yuyao Sun +4
Modern privacy regulations have spurred the evolution of machine unlearning, a technique that enables the removal of data from an already trained ML model without requiring retrain…
Interpreting and Mitigating Hallucination in MLLMs through Multi-agent Debate
Zheng Lin, Zhenxing Niu, Zhibin Wang +1
MLLMs often generate outputs that are inconsistent with the visual content, a challenge known as hallucination. Previous methods focus on determining whether a generated output is…
Efficient LLM-Jailbreaking via Multimodal-LLM Jailbreak
Haoxuan Ji, Zheng Lin, Zhenxing Niu +2
This paper focuses on jailbreaking attacks against large language models (LLMs), eliciting them to generate objectionable content in response to harmful user queries. Unlike previo…