ResumeShield: Channel Separation and an Open Benchmark for Indirect Prompt Injection in AI Resume Screening

Abstract

An AI resume screener reads a document supplied by the person it is evaluating, inverting the usual trust relationship between an assessor and the material it assesses. Candidates exploit this by concealing instructions inside a resume using white text, zero font size, hidden elements, markup comments, document metadata, or zero width characters. A human reviewer sees nothing, while a naive extraction pipeline places the concealed text into the model prompt, where it is read as an instruction and obeyed. This is indirect prompt injection, listed as LLM01:2025 by OWASP, and recent measurement work reports it in roughly one percent of resumes in a production screening corpus. We present ResumeShield, an open-source defense and benchmark. The defense combines three filtering stages with a fourth architectural stage that places candidate content in an explicitly fenced data channel that the operator's trusted instructions declare inert. The benchmark builds a seeded synthetic corpus spanning nine concealment techniques and two payload families, one using documented phrasings and one modeling an adaptive attacker who paraphrases around the filter, and it scores an attack as successful only when the screening outcome changes. On a corpus of 104 documents, the naive pipeline is manipulated in every injected case while the defended pipeline is never manipulated. Detection reaches a precision of 1.000 and a recall of 0.944 with no false positives on clean resumes. An ablation shows that channel separation alone removes all measured attack success, whereas the complete filtering stack without separation still leaves 16.7 percent of attacks effective. We also identify a concealment dilemma: every payload that evaded detection was one the attacker left visible, surrendering the invisibility that motivates the attack. ResumeShield is released under the Apache 2.0 license with synthetic data only.