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cs.CR2026
Concealing LLM-Based Multi-Agent Topology via Phantom Structure Injection
Longzhu He, Zelang Wen, Xinfeng Li +2
Driven by the rapid advancement of large language models (LLMs), LLM-based multi-agent systems (MAS) have emerged as a powerful paradigm for collaborative reasoning over complex ta…
cs.CR2026
SkillDRE: Dual-Stage Red-Team Evolution of Agent Skills via Pre-Execution and Runtime Feedback
Pengyu Zhu, Jingyi Yang, Yi Liu +2
Agent skills package instructions, executable code, and task-specific resources into reusable artifacts that agents can improve using execution feedback. The same mechanism also en…
cs.CR2026
Resource Consumption Threats in Large Language Models
Yuanhe Zhang, Xinyue Wang, Zhican Chen +8
Given limited and costly computational infrastructure, resource efficiency is a key requirement for large language models (LLMs). Efficient LLMs increase service capacity for provi…