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Our analysis reveals several intriguing findings: approximately 1% o","title":"Measuring Real-World Prompt Injection Attacks in LLM-based Resume Screening","url":"https://arxiv.org/abs/2605.28999","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.28999v1 Announce Type: cross \nAbstract: LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated 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. We first design tailored methods to detect prompt injection in resumes. Manual validation on a small-scale dataset demonstrates that our detectors achieve high precision and outperform state-of-the-art general-purpose detectors. We then apply our detector to the full resume dataset and conduct a comprehensive measurement study of real-world prompt injection attacks. 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