Measuring Real-World Prompt Injection Attacks in LLM-based Resume Screening

ArXi:2605.28999v1 Announce Type: cross LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily nstrated conceptually in academic studies or through a few anecdotal case studies. Its prevalence and impact in real-world LLM-based applications are largely unexplored. In this work, we present the first systematic study of prompt-injection attacks in a widely used application: LLM-based resume screening. Our analysis is based on approximately 200K real-world resumes collected over multiple years by hireEZ.