arXiv:2605.28999v1 Announce Type: cross Abstract: 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 de
The proliferation of LLMs in enterprise applications makes this a critical time to evaluate their real-world security vulnerabilities.
This study provides empirical evidence of prompt injection attacks in a common business use case, moving beyond theoretical discussions to demonstrate actual impact.
The understanding of prompt injection vulnerability shifts from conceptual to quantitatively demonstrated, necessitating immediate attention to security in LLM integration.
- · Cybersecurity firms specializing in AI/LLM
- · LLM developers prioritizing robust security
- · Organizations implementing secure LLM practices
- · LLM application users without proper security
- · Organizations relying on insecure LLM-based screening tools
- · Resume screening providers ignoring prompt injection
Companies will re-evaluate or delay the deployment of LLM-based tools that handle sensitive information or automate critical processes.
An increase in demand for red-teaming services and security frameworks specifically designed for LLMs.
The development of industry standards and regulatory guidelines for securing AI agentic systems against adversarial attacks beyond traditional cybersecurity.
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