8 papers
SodaMem: Evidence-Grounded Temporal Graph Memory for LLM Agents
Fengrong Wan, Chengcan Wu, Ningtao Lyu
Large language model (LLM) agents that assist users over weeks of conversation must remember what is currently true, not merely what was once said. Flat RAG diaries and Markdown lo…
AgentWorm: Self-Propagating Attacks Across LLM Agent Ecosystems
Yihao Zhang, Zeming Wei, Xiaokun Luan +7
The paper introduces AgentWorm, a self-replicating worm that can autonomously infect and spread across large-scale LLM-based agent ecosystems by hijacking configurations and execut…
Securing Multi-Agent Systems Against Corruptions via Node Contribution Backpropagation
Chengcan Wu, Zhixin Zhang, Mingqian Xu +2
Multi-Agent Systems (MAS) have become a prevalent paradigm for Large Language Model (LLM) applications. However, the complex multi-agent design in MAS introduces unique trustworthi…
RACC: Representation-Aware Coverage Criteria for LLM Safety Testing
Zeming Wei, Zhixin Zhang, Chengcan Wu +3
Large Language Models (LLMs) face severe safety risks from jailbreak attacks, yet current safety testing largely relies on static datasets and lacks systematic criteria to evaluate…
Secure LLM Fine-Tuning via Safety-Aware Probing
Chengcan Wu, Zhixin Zhang, Zeming Wei +3
Large language models (LLMs) have achieved remarkable success across many applications, but their ability to generate harmful content raises serious safety concerns. Although safet…
Absorber LLM: Harnessing Causal Synchronization for Test-Time Training
Zhixin Zhang, Shabo Zhang, Chengcan Wu +2
Transformers suffer from a high computational cost that grows with sequence length for self-attention, making inference in long streams prohibited by memory consumption. Constant-m…