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

arXiv: https://arxiv.org/abs/2605.28999 · HTML: https://mlsanity.com/paper/2605.28999/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 31 of its 46 references that are in the map.
- First posted on arXiv 27 May 2026.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 30 of 31)

- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [StruQ: Defending Against Prompt Injection with Structured Queries](https://mlsanity.com/paper/2402.06363/) (2024)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [Defeating Prompt Injections by Design](https://mlsanity.com/paper/2503.18813/) (2025)
- [SecAlign: Defending Against Prompt Injection with Preference Optimization](https://mlsanity.com/paper/2410.05451/) (2024)
- [Prompt Injection Attack to Tool Selection in LLM Agents](https://mlsanity.com/paper/2504.19793/) (2025)
- [Optimization-based Prompt Injection Attack to LLM-as-a-Judge](https://mlsanity.com/paper/2403.17710/) (2024)
- [DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks](https://mlsanity.com/paper/2504.11358/) (2025)
- [PLeak: Prompt Leaking Attacks against Large Language Model Applications](https://mlsanity.com/paper/2405.06823/) (2024)
- [Automatic and Universal Prompt Injection Attacks against Large Language Models](https://mlsanity.com/paper/2403.04957/) (2024)
- [PromptArmor: Simple yet Effective Prompt Injection Defenses](https://mlsanity.com/paper/2507.15219/) (2025)
- [System-Level Defense against Indirect Prompt Injection Attacks: An Information Flow Control Perspective](https://mlsanity.com/paper/2409.19091/) (2024)
- [Prompt Flow Integrity to Prevent Privilege Escalation in LLM Agents](https://mlsanity.com/paper/2503.15547/) (2025)
- [Effective Prompt Extraction from Language Models](https://mlsanity.com/paper/2307.06865/) (2023)
- [Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks](https://mlsanity.com/paper/2403.03792/) (2024)
- [ACE: A Security Architecture for LLM-Integrated App Systems](https://mlsanity.com/paper/2504.20984/) (2025)
- [WebInject: Prompt Injection Attack to Web Agents](https://mlsanity.com/paper/2505.11717/) (2025)
- [Lessons from Defending Gemini Against Indirect Prompt Injections](https://mlsanity.com/paper/2505.14534/) (2025)
- [A Critical Evaluation of Defenses against Prompt Injection Attacks](https://mlsanity.com/paper/2505.18333/) (2025)
- [PromptLocate: Localizing Prompt Injection Attacks](https://mlsanity.com/paper/2510.12252/) (2025)
- [ToolTweak: An Attack on Tool Selection in LLM-based Agents](https://mlsanity.com/paper/2510.02554/) (2025)
- [WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents](https://mlsanity.com/paper/2510.01354/) (2025)
- [ObliInjection: Order-Oblivious Prompt Injection Attack to LLM Agents with Multi-source Data](https://mlsanity.com/paper/2512.09321/) (2025)
- [Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface](https://mlsanity.com/paper/2501.09798/) (2025)
- [May I have your Attention? Breaking Fine-Tuning based Prompt Injection Defenses using Architecture-Aware Attacks](https://mlsanity.com/paper/2507.07417/) (2025)
- [SecInfer: Preventing Prompt Injection via Inference-time Scaling](https://mlsanity.com/paper/2509.24967/) (2025)
- [PromptSleuth: Detecting Prompt Injection via Semantic Intent Invariance](https://mlsanity.com/paper/2508.20890/) (2025)
- [System Prompt Extraction Attacks and Defenses in Large Language Models](https://mlsanity.com/paper/2505.23817/) (2025)

## Built on by (most cited first, 5 of 5)

- [Beyond the Editing Canvas: Evidence Divergence in OOXML-to-LLM Ingestion](https://mlsanity.com/paper/2608.25880/) (2026)
- [Staying on the Attack Path: Structured State for Long-Horizon Automated Penetration Testing](https://mlsanity.com/paper/2609.07344/) (2026)
- [ResumeShield: Channel Separation and an Open Benchmark for Indirect Prompt Injection in AI Resume Screening](https://mlsanity.com/paper/2609.20188/) (2026)
- [Understanding and Exploiting Initialization Anchoring Weakness in Feedback-Based Agent Planning](https://mlsanity.com/paper/2609.29697/) (2026)
- [Prompt-Injection Probes Learn the Conversation, Not Just the Injection](https://mlsanity.com/paper/2610.18408/) (2026)

## Abstract (arXiv)

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. Our analysis reveals several intriguing findings: approximately 1% of resumes contain hidden prompt injections; the prevalence of such injected resumes has increased noticeably over the past one to two years; and more than 90% of injected prompts do not use explicit instructions. These results provide the first evidence of large-scale prompt injection in real-world LLM-based applications and lay the groundwork for future studies to understand and mitigate such attacks.