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

AI resume screening systems are vulnerable to 'indirect prompt injection,' where job candidates embed hidden instructions within their resumes to manipulate the AI's assessment. These hidden instructions, undetectable to human reviewers, can influence the AI's evaluation by appearing as part of the model's prompt. This vulnerability, identified as LLM01:2025 by OWASP, has been observed in approximately one percent of production screening corpuses, necessitating robust defense mechanisms.

The integrity of automated hiring processes is at risk due to indirect prompt injection in AI resume screening, potentially leading to biased or manipulated candidate evaluations. This vulnerability poses a significant challenge to talent acquisition strategies and the perceived fairness of recruitment technologies. Organizations relying on AI for critical assessment functions must address this security flaw to maintain trust and ensure accurate decision-making.

AI resume screeners are susceptible to indirect prompt injection due to their inversion of the traditional trust relationship between assessor and assessed material.

Forward consideration, not a verified fact.