3 papers
cs.CR2025
PromptArmor: Simple yet Effective Prompt Injection Defenses
Tianneng Shi, Kaijie Zhu, Zhun Wang +13
Despite their potential, recent research has demonstrated that LLM agents are vulnerable to prompt injection attacks, where malicious prompts are injected into the agent's input, c…
cs.CR2025
PromptShield: Deployable Detection for Prompt Injection Attacks
Dennis Jacob, Hend Alzahrani, Zhanhao Hu +2
Application designers have moved to integrate large language models (LLMs) into their products. However, many LLM-integrated applications are vulnerable to prompt injections. While…
cs.CL2025
Can LLMs Ask Good Questions?
Yueheng Zhang, Xiaoyuan Liu, Yiyou Sun +5
We evaluate questions generated by large language models (LLMs) from context, comparing them to human-authored questions across six dimensions: question type, question length, cont…