13 citations · 15 across the 5 of their papers we have counts for
6 papers
Agent-Canary-Mas: End-to-End Evaluation of Protocol Risk in Long-Horizon Multi-Agent Systems
Xiuming Liu, Le Gai, Zhiyuan Chang +4
Agent-to-agent protocols allow LLM agents to discover peers, exchange results, and coordinate tool use. A locally successful protocol attack, however, may be rejected by a later ag…
Beyond Handcrafted Security: Towards Self-Evolving Defense for LLM Agents
Jiajun Ruan, Peiyang Li, Yukun Chen +2
The expanding operational capabilities of large language model (LLM) agents introduce sophisticated security threats. Runtime defenses have emerged as an effective approach to miti…
AgentCanary: A Security Evaluation Framework for Autonomous AI Agents in Real Executable Environments
Peiyang Li, Songping Wang, Yi Huang +9
Autonomous AI agents have driven the transition from conversation to task execution, shifting security failures from textual deception to system compromise. Although security evalu…
Serving Large Language Models on Huawei CloudMatrix384
Pengfei Zuo, Huimin Lin, Junbo Deng +43
The rapid evolution of large language models (LLMs), driven by growing parameter scales, adoption of mixture-of-experts (MoE) architectures, and expanding context lengths, imposes…
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Tianyu Cui, Yanling Wang, Chuanpu Fu +13
Large language models (LLMs) have strong capabilities in solving diverse natural language processing tasks. However, the safety and security issues of LLM systems have become the m…
Learning from Limited Heterogeneous Training Data: Meta-Learning for Unsupervised Zero-Day Web Attack Detection across Web Domains
Peiyang Li, Ye Wang, Qi Li +5
Recently unsupervised machine learning based systems have been developed to detect zero-day Web attacks, which can effectively enhance existing Web Application Firewalls (WAFs). Ho…