3 papers
cs.CL2026
How Much Can We Trust LLM Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation
Yimeng Chen, Zhe Ren, Firas Laakom +3
Large language model (LLM)-based search agents synthesize open-web content into actionable recommendations on behalf of users, creating a risk that attacker-published pages are tra…
cs.CR2026
LLM: LSTM Look-Ahead Moving Target Defense Based on Historical Malicious Scan
Yu Li
Network scanning is a critical preliminary step for most adversaries to gain essential information before launching cyber attacks. Moving Target Defense (MTD) based on IP shuffling…
cs.SE2026
AgentGuard: A Multi-Agent Framework for Robust Package Confusion Detection via Hybrid Search and Metadata-Content Fusion
Yu Li, Wei Ma, Zhi Chen +6
The proliferation of open-source software (OSS) has made software supply chains prime targets for attacks like Package Confusion, where adversaries publish malicious packages with…