2 papers
cs.CR2026
Measuring the Security of Mobile LLM Agents under Adversarial Prompts from Untrusted Third-Party Channels
Chenghao Du, Quanfeng Huang, Tingxuan Tang +3
Large Language Models (LLMs) have transformed software development, enabling AI-powered applications known as LLM-based agents that promise to automate tasks across diverse apps an…
cs.CR2026
From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching
Zhixiang Zhang, Zesen Liu, Yuchong Xie +2
Semantic caching has emerged as a pivotal technique for scaling LLM applications, widely adopted by major providers including AWS and Microsoft. By utilizing semantic embedding vec…