5 papers
OEP: Poisoning Self-Evolving LLM Agents via Locally Correct but Non-Transferable Experiences
Kaixiang Wang, Jiong Lou, Zhaojiacheng Zhou +1
Memory-augmented large language model (LLM) agents use iterative reflection and self-evolution to solve complex tasks, but these mechanisms introduce security risks. Existing agent…
Proteus: A Self-Evolving Red Team for Agent Skill Ecosystems
Zhaojiacheng Zhou
Agent skills extend LLM agents with reusable instructions, tool interfaces, and executable code, and users increasingly install third-party skills from marketplaces, repositories,…
E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory
Kaixiang Wang, Yidan Lin, Jiong Lou +3
The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requires maintaining rigorous logical…
MAS-Shield: A Defense Framework for Secure and Efficient LLM MAS
Kaixiang Wang, Zhaojiacheng Zhou, Bunyod Suvonov +9
Large Language Model (LLM)-based Multi-Agent Systems (MAS) are susceptible to linguistic attacks that can trigger cascading failures across the network. Existing defenses face a fu…
BLOCKS: Blockchain-supported Cross-Silo Knowledge Sharing for Efficient LLM Services
Zhaojiacheng Zhou, Hongze Liu, Shijing Yuan +4
The hallucination problem of Large Language Models (LLMs) has increasingly drawn attention. Augmenting LLMs with external knowledge is a promising solution to address this issue. H…